diff --git a/Benchmarks/CyRK - SciPy Comparison.ipynb b/Benchmarks/CyRK - SciPy Comparison.ipynb
index df61e4f..c850ade 100644
--- a/Benchmarks/CyRK - SciPy Comparison.ipynb	
+++ b/Benchmarks/CyRK - SciPy Comparison.ipynb	
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.11.4\n"
+      "0.12.0\n"
      ]
     }
    ],
@@ -609,7 +609,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -708,37 +708,37 @@
      "text": [
       "How much faster X is vs. Y\n",
       "End Time: 1.0e-03\n",
-      "\t nbsolve_ivp = 10.37x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
-      "\t pysolve_ivp = 16.33x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
-      "\t cysolve_ivp = 47.57x SciPy\t cysolve_ivp =  2.91x pysolve_ivp\n",
+      "\t nbsolve_ivp = 10.46x SciPy\t nbsolve_ivp =  0.21x cysolve_ivp\n",
+      "\t pysolve_ivp = 16.65x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
+      "\t cysolve_ivp = 49.25x SciPy\t cysolve_ivp =  2.96x pysolve_ivp\n",
       "End Time: 1.0e-02\n",
-      "\t nbsolve_ivp = 10.41x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
-      "\t pysolve_ivp = 15.75x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
-      "\t cysolve_ivp = 46.80x SciPy\t cysolve_ivp =  2.97x pysolve_ivp\n",
+      "\t nbsolve_ivp = 10.50x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
+      "\t pysolve_ivp = 16.40x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
+      "\t cysolve_ivp = 48.57x SciPy\t cysolve_ivp =  2.96x pysolve_ivp\n",
       "End Time: 1.0e-01\n",
-      "\t nbsolve_ivp = 14.76x SciPy\t nbsolve_ivp =  0.23x cysolve_ivp\n",
-      "\t pysolve_ivp = 19.68x SciPy\t pysolve_ivp =  0.30x cysolve_ivp\n",
-      "\t cysolve_ivp = 65.30x SciPy\t cysolve_ivp =  3.32x pysolve_ivp\n",
+      "\t nbsolve_ivp = 15.20x SciPy\t nbsolve_ivp =  0.23x cysolve_ivp\n",
+      "\t pysolve_ivp = 20.17x SciPy\t pysolve_ivp =  0.30x cysolve_ivp\n",
+      "\t cysolve_ivp = 66.22x SciPy\t cysolve_ivp =  3.28x pysolve_ivp\n",
       "End Time: 1.0e+00\n",
-      "\t nbsolve_ivp = 29.15x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
-      "\t pysolve_ivp = 26.41x SciPy\t pysolve_ivp =  0.23x cysolve_ivp\n",
-      "\t cysolve_ivp = 115.34x SciPy\t cysolve_ivp =  4.37x pysolve_ivp\n",
+      "\t nbsolve_ivp = 30.94x SciPy\t nbsolve_ivp =  0.24x cysolve_ivp\n",
+      "\t pysolve_ivp = 27.57x SciPy\t pysolve_ivp =  0.22x cysolve_ivp\n",
+      "\t cysolve_ivp = 127.37x SciPy\t cysolve_ivp =  4.62x pysolve_ivp\n",
       "End Time: 1.0e+01\n",
-      "\t nbsolve_ivp = 103.98x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
-      "\t pysolve_ivp = 38.87x SciPy\t pysolve_ivp =  0.11x cysolve_ivp\n",
-      "\t cysolve_ivp = 361.05x SciPy\t cysolve_ivp =  9.29x pysolve_ivp\n",
+      "\t nbsolve_ivp = 97.99x SciPy\t nbsolve_ivp =  0.27x cysolve_ivp\n",
+      "\t pysolve_ivp = 36.21x SciPy\t pysolve_ivp =  0.10x cysolve_ivp\n",
+      "\t cysolve_ivp = 364.07x SciPy\t cysolve_ivp = 10.05x pysolve_ivp\n",
       "End Time: 1.0e+02\n",
-      "\t nbsolve_ivp = 129.63x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
-      "\t pysolve_ivp = 38.85x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
-      "\t cysolve_ivp = 447.00x SciPy\t cysolve_ivp = 11.51x pysolve_ivp\n",
+      "\t nbsolve_ivp = 131.79x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.78x SciPy\t pysolve_ivp =  0.08x cysolve_ivp\n",
+      "\t cysolve_ivp = 460.15x SciPy\t cysolve_ivp = 11.87x pysolve_ivp\n",
       "End Time: 1.0e+03\n",
-      "\t nbsolve_ivp = 133.10x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
-      "\t pysolve_ivp = 38.65x SciPy\t pysolve_ivp =  0.08x cysolve_ivp\n",
-      "\t cysolve_ivp = 460.74x SciPy\t cysolve_ivp = 11.92x pysolve_ivp\n",
+      "\t nbsolve_ivp = 123.01x SciPy\t nbsolve_ivp =  0.27x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.55x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
+      "\t cysolve_ivp = 450.80x SciPy\t cysolve_ivp = 11.70x pysolve_ivp\n",
       "End Time: 1.0e+04\n",
-      "\t nbsolve_ivp = 110.75x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
-      "\t pysolve_ivp = 38.30x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
-      "\t cysolve_ivp = 442.66x SciPy\t cysolve_ivp = 11.56x pysolve_ivp\n"
+      "\t nbsolve_ivp = 112.00x SciPy\t nbsolve_ivp =  0.24x cysolve_ivp\n",
+      "\t pysolve_ivp = 40.00x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
+      "\t cysolve_ivp = 460.60x SciPy\t cysolve_ivp = 11.51x pysolve_ivp\n"
      ]
     }
    ],
diff --git a/Benchmarks/CyRK_CySolver.pdf b/Benchmarks/CyRK_CySolver.pdf
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diff --git a/Benchmarks/CyRK_SciPy_Compare_predprey_v0-12-0.png b/Benchmarks/CyRK_SciPy_Compare_predprey_v0-12-0.png
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diff --git a/Benchmarks/CyRK_numba.pdf b/Benchmarks/CyRK_numba.pdf
index 92bb367..4e2bde9 100644
Binary files a/Benchmarks/CyRK_numba.pdf and b/Benchmarks/CyRK_numba.pdf differ
diff --git a/Benchmarks/SciPy.pdf b/Benchmarks/SciPy.pdf
index d544d3a..840b425 100644
Binary files a/Benchmarks/SciPy.pdf and b/Benchmarks/SciPy.pdf differ
diff --git a/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png b/Benchmarks/archive/CyRK_SciPy_Compare_predprey_v0-11-4.png
similarity index 100%
rename from Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png
rename to Benchmarks/archive/CyRK_SciPy_Compare_predprey_v0-11-4.png
diff --git a/CHANGES.md b/CHANGES.md
index 99545e3..68f4f20 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -18,6 +18,7 @@ Fixes:
 Tests:
 * Fixed tests where additional args were not being used.
 * Fixed issue with diffeq test 5.
+  * This fixes GitHub Issue [#67](https://github.com/jrenaud90/CyRK/issues/67)
 
 Documentation:
 * Updated the "Advanced CySolver.md" documentation that was out of date.
diff --git a/CyRK/cy/cysolve.cpp b/CyRK/cy/cysolve.cpp
index 8165b76..babde57 100644
--- a/CyRK/cy/cysolve.cpp
+++ b/CyRK/cy/cysolve.cpp
@@ -1,7 +1,6 @@
 #include "cysolve.hpp"
 #include <exception>
 
-#include <cstdio>
 
 void baseline_cysolve_ivp_noreturn(
         std::shared_ptr<CySolverResult> solution_sptr,
@@ -35,7 +34,6 @@ void baseline_cysolve_ivp_noreturn(
     const double t_end         = t_span_ptr[1];
     const bool direction_flag  = t_start <= t_end ? true : false;
     const bool forward = direction_flag == true;
-    printf("t_start = %e; t_end = %e; direction_flag = %d; forward = %d\n", t_start, t_end, direction_flag, forward);
     const bool t_eval_provided = t_eval ? true : false;
 
     // Get new expected size
diff --git a/CyRK/cy/cysolver.cpp b/CyRK/cy/cysolver.cpp
index 3f3c35d..656958b 100644
--- a/CyRK/cy/cysolver.cpp
+++ b/CyRK/cy/cysolver.cpp
@@ -1,4 +1,3 @@
-#include <cstdio>
 #include "cysolver.hpp"
 #include "dense.hpp"
 #include "cysolution.hpp"
@@ -67,29 +66,11 @@ CySolverBase::CySolverBase(
     {
         // Allocate memory for the size of args.
         // Store void pointer to it.
-        printf("Pre resize; size = %d\n", this->size_of_args);
-        printf("Pre resize; VECTOR size = %d\n", this->args_char_vec.size());
         this->args_char_vec.resize(this->size_of_args);
 
-        for (size_t i = 0; i < this->size_of_args; i++)
-        {
-            printf("\t %x\n", args_ptr[i] & 0xff);
-        }
-        
-
         // Copy over contents of arg
-        printf("Pre Copy Over: arg_in_char = %p; sizeof = %d; args_in_char+size = %p\n", args_ptr, this->size_of_args, args_ptr + this->size_of_args);
-        // this->args_char_vec.insert(this->args_char_vec.begin(), args_ptr, args_ptr + this->size_of_args);
         this->args_ptr = this->args_char_vec.data();
         std::memcpy(this->args_ptr, args_ptr, this->size_of_args);
-
-        printf("Vector array size = %d\n", this->args_char_vec.size());
-        for (size_t i = 0; i < this->args_char_vec.size(); i++)
-        {
-            printf("\t %x\n", this->args_char_vec[i] & 0xff);
-        }
-        
-        printf("Post\n");
     }
     else
     {
@@ -183,7 +164,6 @@ CySolverBase::CySolverBase(
 // Destructors
 CySolverBase::~CySolverBase()
 {
-    printf("CySolver Deconstructor called.\n");
     if (this->deconstruct_python)
     {
         // Decrease reference count on the cython extension class instance
diff --git a/CyRK/cy/cysolver_api.pyx b/CyRK/cy/cysolver_api.pyx
index 97b8aef..aa50f81 100644
--- a/CyRK/cy/cysolver_api.pyx
+++ b/CyRK/cy/cysolver_api.pyx
@@ -6,8 +6,6 @@ import numpy as np
 cimport numpy as cnp
 cnp.import_array()
 
-from libc.stdio cimport printf
-
 # =====================================================================================================================
 # Import CySolverResult (container for integration results)
 # =====================================================================================================================
diff --git a/CyRK/cy/cysolver_test.pyx b/CyRK/cy/cysolver_test.pyx
index 78a489b..49e818c 100644
--- a/CyRK/cy/cysolver_test.pyx
+++ b/CyRK/cy/cysolver_test.pyx
@@ -2,12 +2,9 @@
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 
 from libcpp cimport bool as cpp_bool
-from libcpp.memory cimport make_shared, shared_ptr
-from libcpp.vector cimport vector
 from libcpp.limits cimport numeric_limits
-from libc.math cimport sin, cos, fabs, fmin, fmax
+from libc.math cimport sin, cos
 from libc.stdlib cimport malloc, free, realloc
-from libc.stdio cimport printf
 
 cdef double d_NAN = numeric_limits[double].quiet_NaN()
 
@@ -156,7 +153,6 @@ cdef void arbitrary_arg_test(double* dy_ptr, double t, double* y_ptr, char* args
     cdef double g = arb_args_ptr.g
     cdef cpp_bool cause_fail = arb_args_ptr.cause_fail
     cdef cpp_bool checker    = arb_args_ptr.checker
-    printf("arbitrary_arg_test:: l = %e, m = %e, g = %e; cause_fail = %d\n", l, m, g, cause_fail)
 
     cdef double coeff_1 = (-3. * g / (2. * l))
     cdef double coeff_2 = (3. / (m * l**2))
@@ -224,31 +220,22 @@ cdef void pendulum_preeval_diffeq(double* dy_ptr, double t, double* y_ptr, char*
 
 def cy_extra_output_tester():
 
-    printf("cy_extra_output_tester Pt1\n")
-
     cdef double[2] t_span = [0., 10.]
     cdef double* t_span_ptr = &t_span[0]
 
-    printf("cy_extra_output_tester Pt2\n")
     cdef double[3] y0 = [1., 0., 0]
     cdef double* y0_ptr = &y0[0]
     
-    printf("cy_extra_output_tester Pt3\n")
     cdef size_t num_y     = 3
     cdef size_t num_extra = 3
     cdef int int_method   = 1
 
-    printf("cy_extra_output_tester Pt4\n")
-    cdef size_t arg_size = sizeof(double)*3
-    printf("cy_extra_output_tester Pt5\n")
+    cdef size_t arg_size = sizeof(double) * 3
     cdef double* args_ptr = <double*>malloc(arg_size)
-    printf("cy_extra_output_tester Pt6\n")
     args_ptr[0] = 10.0
     args_ptr[1] = 28.0
     args_ptr[2] = 8.0 / 3.0
-    printf("cy_extra_output_tester Pt7\n")
-    
-    printf("cy_extra_output_tester Pt8\n")
+
     cdef CySolveOutput result = cysolve_ivp(
         lorenz_extraoutput_diffeq,
         t_span_ptr,
@@ -261,18 +248,14 @@ def cy_extra_output_tester():
         arg_size,
         num_extra
         )
-    printf("cy_extra_output_tester Pt9\n")
 
     cdef double dy1, dy2, dy3, e1, e2, e3
     cdef double check_t = 4.335
 
     # Call the result to get the baseline values for extra output
-    printf("cy_extra_output_tester Pt10\n")
     cdef double[6] y_interp
     cdef double* y_interp_ptr = &y_interp[0]
-    printf("cy_extra_output_tester Pt11\n")
     result.get().call(check_t, y_interp_ptr)
-    printf("cy_extra_output_tester Pt12\n")
     dy1 = y_interp_ptr[0]
     dy2 = y_interp_ptr[1]
     dy3 = y_interp_ptr[2]
@@ -284,17 +267,14 @@ def cy_extra_output_tester():
     args_ptr[0] = -99.0
     args_ptr[1] = -99.0
     args_ptr[2] = -99.0
-    printf("cy_extra_output_tester Pt13\n")
-    args_ptr = <double*>realloc(args_ptr, sizeof(double)*50)
+    args_ptr = <double*>realloc(args_ptr, sizeof(double) * 50)
     cdef size_t i 
     for i in range(50):
         args_ptr[i] = -99.0
     
 
     # Recall the solution to see if it still produces the expected values
-    printf("cy_extra_output_tester Pt14\n")
     result.get().call(check_t, y_interp_ptr)
-    printf("cy_extra_output_tester Pt15\n")
 
     np.testing.assert_allclose(dy1, y_interp_ptr[0])
     np.testing.assert_allclose(dy2, y_interp_ptr[1])
@@ -302,7 +282,8 @@ def cy_extra_output_tester():
     np.testing.assert_allclose(e1, y_interp_ptr[3])
     np.testing.assert_allclose(e2, y_interp_ptr[4])
     np.testing.assert_allclose(e3, y_interp_ptr[5])
-    printf("cy_extra_output_tester Pt16\n")
+
+    free(args_ptr)
 
     return True
 
@@ -324,7 +305,6 @@ def cytester(
         double max_step = MAX_STEP,
         double first_step = 0.0
         ):
-    printf("Cytester pt0\n")
     cdef size_t i
     cdef double* t_eval_ptr = NULL
     cdef size_t len_t_eval = 0
@@ -342,7 +322,6 @@ def cytester(
     cdef size_t num_extra = 0
     cdef DiffeqFuncType diffeq = NULL
     cdef PreEvalFunc pre_eval_func = NULL
-    printf("Cytester pt1\n")
     if diffeq_number == 0:
         diffeq = baseline_diffeq
     elif diffeq_number == 1:
@@ -366,7 +345,6 @@ def cytester(
         pre_eval_func = pendulum_preeval_func
     else:
         raise NotImplementedError
-    printf("Cytester pt2\n")
 
     # Set up additional argument information
     cdef char* args_ptr = NULL
@@ -380,7 +358,6 @@ def cytester(
     cdef ArbitraryArgStruct arb_arg_struct = ArbitraryArgStruct(1.0, False, True, 1.0, 9.81)
     
     # Check if generic testing was requested.
-    printf("Cytester pt3\n")
     if y0 is None:
         # Generic Testing Requested
         
@@ -482,7 +459,6 @@ def cytester(
         else:
             raise NotImplementedError
     else:
-        printf("Cytester pt4bn")
         # Regular testing requested.
         num_y = len(y0)
         for i in range(num_y):
@@ -501,7 +477,6 @@ def cytester(
         size_of_args = sizeof(args_arr)
 
     # Parse rtol
-    printf("Cytester pt5\n")
     cdef double* rtols_ptr = NULL
     if rtol_array is not None:
         rtols_ptr = &rtol_array[0]
@@ -511,10 +486,6 @@ def cytester(
     if atol_array is not None:
         atols_ptr = &atol_array[0]
 
-    
-    printf("EXTRA ARGS SIZE = %d; Ptr = %p\n", size_of_args, args_ptr)
-
-    printf("PRE CySolve\n")
     cdef CySolveOutput result = cysolve_ivp(
         diffeq,
         t_span_ptr,
@@ -538,8 +509,6 @@ def cytester(
         first_step = first_step,
         expected_size = expected_size
         )
-    printf("POST CySolve\n")
-    
     cdef WrapCySolverResult pysafe_result = WrapCySolverResult()
     pysafe_result.set_cyresult_pointer(result)
 
diff --git a/Demos/1 - Getting Started.ipynb b/Demos/1 - Getting Started.ipynb
index dc520c2..47b5797 100644
--- a/Demos/1 - Getting Started.ipynb	
+++ b/Demos/1 - Getting Started.ipynb	
@@ -9,8 +9,8 @@
     "As of CyRK v0.11.0, on a mid-tier desktop the following timings were found:\n",
     "\n",
     "- SciPy.solve_ivp : 39.300  ms\n",
-    "- CyRK.nbsolve_ivp:  0.361  ms\n",
-    "- CyRK.pysolve_ivp:  1.250  ms\n",
+    "- CyRK.nbsolve_ivp:  0.333  ms\n",
+    "- CyRK.pysolve_ivp:  1.190  ms\n",
     "- CyRK.cysolve_ivp:  0.107  ms"
    ]
   },
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CyRK Version: 0.11.0a0.dev3\n"
+      "CyRK Version: 0.12.0\n"
      ]
     }
    ],
@@ -49,7 +49,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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VkhL5PT/bgwSKT6AgI9Fuz0xTXtmn/4Bo+oeRZbWplLv35CXUViTZNiYDotlWsvkGEAx7kEDxCTQqlPT0yA6G5tWDH2RUk6+tAFQ0DETTN4R4LSk5elVbFO2RK/sahFhKAsUn0GGBkmz7fgDRDDLZfAOIpj2ydchlZXIZdhTbC7UVEvN9CXr2lQSKTzAyCmLM3XvyCm2atu8Gw0FoVKoxEnSpQ+ZFw+QfkiCMkFVjpF4rSm0l6IKNBIpPyDcK6unRd9xhRqv6tft+AMFoVKrJFWSi3AmRPTg0xSPJlTGIsm8EVbCRQPEJ2YJMZaV8HZWGRRkDPTTFo4dWNekxUjQctexrUDMGqqEpHkIJ2RpWQUH06lAoyOghe+gJetBVSSoFJBL8dbYMCmNAR4e79+UVJF71BD12kEDxCeLE2pKSo38WtdSkaFSZDriOmi2A4AcZ1eTyj6jZI9tJ1wDvpAsL+euo2EOIV/INTtCnQ0mg+AQRaKhhyRFhPltEJW1NvqFH2IOmAPUCpa9/RLFo2IhvRMUWQPBjBwkUH8BY8B1JJUZs0d2tD85hJld2LWodMmDMHi0t7t2Pl2jbgDiXSYsYJUfNHrl8IypxFAh+v0ICxQckkzIbEFRHUkmuRhXFouFc9ghCmlY1uTJsUavX0vpG3xVvQPTsYdQ3KPsaDN8ggeIDcqVpgWA4kkpyNaqCAqBfP/46KruFUoesJ+hBVyW5MgaAtEdU2ooR30il9JtBhpmgtxUSKD4gn0ARWQM/O5JKjAbdqNmDfIND9pDkEq8AtRUtFRXyddTskWvxhZ9tQQLFBwgnKiiQW3VrCYIjqYSCrp6gj4JUY8QelDHgRM0/8mVfoyZggx47SKD4AKNBhoIuJwgNSyVGRkFdXXy34SgQ9KCrEmoresTghqa8OLkGe0EQayRQfAAFGT1kDz1GOmQgOkE36GlrlVBb0UP20BP0bCMJFB9ANRd68gWZICh/leQaBZWWyuWlUbAHY3yJOUAZFCB/7IhaWyGBoifo2VcSKD6AGpUesocesodEiDWABApA08N9obaix2j21a/2IIHiAyhjoIeWTuoh/5DQijc9VFCuJ589ouYfuWJHSYmMsX61BwkUH0BBRg/ZQw9NAUq0AiVX2rq9PRqbcVHGQA+1FT1Bj6UkUHwApWn1UNDVQ/4hEbYoKuLLRvsStc24qK3oobaiJ+jZVxIoPoCCjJ6gNyrVkH9I8o0IxS7DQDTsQb6hh+yhJ+jT5SRQfAA1Kj1kDz1BT9OqJJ9vRG0zLlrFo4dih56g24MEig8w6kSdnf5dDqaSoDcq1dC8uiSfbwDRsodR8erXEbJqSMxLkkn+AIJrDxIoPsBohwxEI9AEPS2pGprykhgRKGQPid87INVQRkmSb0k+4H//IIHiA/I1qpISeUaPXx1JJTQK0kOdkMRMBiUKAtaob/h5My6VUFuR5FvxBvjfHiRQfEC+RhWL+d+RVEJBRsIYrUzQkk/MA9HyDzPZV7JHNNsKENyMEgkUH0CjQj00pSER27oDJNiA/Nk1IFr2MJJ99ftmXCqh7KtE6xuxWOb3+L1fIYHiAyjo6jGTQQn7ZlzaeeSgpmlVQkWyeozEjigJesq+SsLQVkig+IAwOJJKjAaZKGzGlW9rd4B8oy9kDz1+HyWrhASKRIjXIE+HkkDxARR09eRLW2s34wp70BW2KCiQhdJ9oRGyHrKHHoodEuEbUTgKIQy+QQLFB4TBkVSSL20dpc24yDf0mCmSDbt4Bcg/+mIm+3rkiDv35BVh8A0SKD6ARoV6wtCwVEEpfD1Ur6WHVjXpMXIUgigYDXt7CUO/QgLFB1AnpCcMDUsVYZhHVgmJVz0k2CSM5bdHLBad2BGGtkICxQfQKEhPGBqWKszYIgqbcZFv6CExLzGycyoQHf8wM7jx68CXBIoPoFGQHhJsEjMdMkD2AKLjGwBlX7UY2ZgMiI5/hKGtkEDxASRQ9JA9JEbEWkkJUFzMX5M9opMxAMLRCamCMih6zPhGPK7fFNIvmBIo8+bNw+mnn47+/ftjyJAhuPLKK7F161bde8477zzEYjHd42tf+5ruPTt37sQll1yCiooKDBkyBN/61rfQE/bcdA6ozkCiPYEzyKlJVRgRa0B0/IPaih6yh0R0yIWFfKVfNqIiYI34hrAF4E97mBIoy5cvx9y5c7Fy5UosWbIE3d3duOiii9DR0aF7380334y9e/emHw8++GD6Z8lkEpdccgkSiQTefvttPPnkk1iwYAHuvfdeNX9RADHjSH50IpVoVTwFXWO+AZBg0xIVWwCUbdRiVsyH3T+M2MPvRyFk2fopM4sXL9Z9vWDBAgwZMgRr167Fueeem/5+RUUF6uvrM17j1VdfxaZNm/Daa6+hrq4Op5xyCn7wgx/g29/+Nr73ve+hJF8kDiE0CpIY2dodiI5gowyKHrOFf4xlP4ckDFDskJgV82QPTv/+wIED/rSHrRqUlpYWAEBtba3u+0899RQGDRqEk046CXfffTeOaHbEWbFiBU4++WTU1dWlvzdjxgy0trZi48aNGT8nHo+jtbVV9wgTYai2VoVWoIi6ikxQkNFD9pBEaTMuyr5KqK3oCUP21VQGRUsqlcLtt9+Os846CyeddFL6+1/60pcwevRoDBs2DOvXr8e3v/1tbN26Fc899xwAoLGxUSdOAKS/bmxszPhZ8+bNw/e//32rt+p7aBQkEbYoLOSPbETNHhR0OUbsUVHBsyaMcXtoj0YIGxQ7JNRW9ITBHpYFyty5c/HBBx/grbfe0n3/lltuSb8++eSTMXToUFxwwQXYtm0bjj32WEufdffdd+POO+9Mf93a2oqRI0dau3EfQkFGQlMaeowGGRolSwoKuChpb+f2yDLbHAoo+yoJQ4eskjDYw9IUz2233YZFixbh9ddfx4gRI3K+d9q0aQCAjz/+GABQX1+PpqYm3XvE19nqVkpLS1FVVaV7hAkzQSbsm3GFIS2pkjAEGZWQPfTQ4EZCYl5PGNqKKYHCGMNtt92G559/HsuWLcPYsWPz/s66desAAEOHDgUANDQ0YMOGDWhubk6/Z8mSJaiqqsLEiRPN3E5oMBNkAH86kirC0KhUQoJND63UkGi3dieBQm2lL2EQbKameObOnYunn34aL774Ivr375+uGamurkZ5eTm2bduGp59+GhdffDEGDhyI9evX44477sC5556LSZMmAQAuuugiTJw4EV/5ylfw4IMPorGxEffccw/mzp2L0nxRJ6QYcSSxGVd3N3ekAQPcuTe3CUOjUgkJNj1kD4k2k2pEoIjNuHIVnwcZ8g09YbCHqQzKo48+ipaWFpx33nkYOnRo+vHMM88AAEpKSvDaa6/hoosuwoQJE/DNb34Tc+bMwUsvvZS+RmFhIRYtWoTCwkI0NDTgy1/+Mm644Qbcf//9av+yAEHKXxKGRqUSsocesofE7JJ8IBr2IN/ghKFfMZVBYYzl/PnIkSOxfPnyvNcZPXo0Xn75ZTMfHWrMONLBg+FuWBRk9JA99JA9JEYFitiMK5Hg9uizK0RoIN/QEwZ70Fk8HmN0HhnwtyOpworqz6ObAw1Neekhe0iELWKx3EvyAX+PklVBcVRPGOxBAsVjkknZwQbZkVRhtlGFfTOuMAQZlZA9JFpb5NstN2r2yEUUxCtg7GBNwN++QQLFY4ymaQF/O5IqjAYZsRkXQKNCIBojZIDsocWoLQCKHVoo+6rHz4KNBIrHmBEofnYkVRhtVGIzLoDsAUSjAwLIHlpIoOgxuwSdMcq+Av72DRIoHiPScABQlKdk2c+OpAoKunrCEGRUQvaQmGkrNLiRVFTwAQ5A9gD8nW0kgeIxVuaR/ehIqiCBooc6ZD1kDwm1FT1G7RGLRUuwBfnYEBIoHkNBRg/ZQ4/ZeWSxGVdYCcO8uiqstBUa3HAodkj8bAsSKB5DjUoP2UOP2SADkD0A8o2+kD30kICV+Nk3SKB4DAUZPUaXxgE0KtQiNuMCwmuPVEpu7x7keXVVUOzQQxklPWYFSiKhX7ThB0igeAwFGT00CtJD/iHRTl0FeVSoCvINPWQPPWanQwH/2YMEisdQh6yHgowesoeE9gzSQ7FDD7UVPUbtUVwsC2n9llEigeIx1Kj0kD30kD0kWoGS70TeKGzGZWU6NKy+AVBb6UsY7EECxWNo3lRPGBqVSsgeEmGLwsL8Z8+IjAFjQEeHs/flFRQ79FBb0RMGe5BA8ZgwOJFKyB56KI0vMWOLfv3kvkJkD2orfQl7WwHC4R8kUDwmDE6kErKHHholS8zYQrsZF9kjGm2FVgDqCYN/kEDxGKO7/QHSicK8GZeVURAFGY5fg4wqzNgCIHtoCbstALKHFsbCkVEigeIxVpwI8J8jqcKKYAurLQCyhxYSKHrC0AGphASKJJmUxeFBziiRQPEYM41KuxlXWBsWBRk9ZA8JCRQ9VnzDj5txqYLaisTMknzAv/YggeIxVoOu35SuKijI6KGlpBKzbSXsWQMrbQWg2AFEp60AwbYHCRSPoVGhHgoykrDMI6uC2ooeM/bQbsZF9ohOWwHy7xkE+LetkEDxGAq6eqyuWgnjZlxhmUdWBWUb9VDs0EODG4mwRXGxXG6fC7/agwSKx1CQ0WNlFMQYcOSIc/fkFWFJ06qC2ooesoceWpIvCct0KAkUj6Ego4c245KQQNFjph4HCL89KHbooeyrJCy+QQLFY8KidFVhdTOuMNojLPPIqghL0FUFxQ49VgRKWI9CCMt0KAkUj6Ggq4fsIQnLPLIqyDf0mNkjB/BvJ6QKM/5RXg4U9PZ+YfSPsLQVEigeExalq4qwNCwV0AhZD9lDD7UVPZR9lYTFN0igeExYHEkVZA+JVVuEdTMuEvN6qK3oIXtIwmILEigeExZHUgWNkiVWfQMIZ6dMbUUP2UPCmDyfjARseOIoCRSPoSCjh0bJErO2CPtmXNRW9JA9JNrDU8ke1n2ju1uulvMDJFA8JixKVxUUdCVmbQGQPbSE2RYAxQ4tZpfkA+H2j7BkX0mgeAwFXT2014WEBIoeait6KNsoIYGix6xvFBUBZWX8tZ/sQQLFYyjISOzMI/upUanCikCJwijZbMagvR1IpZy5Jy8hwSYRtojFgMJCY78ThbZidAk64E//IIHiMRRkJDSPrMdOBiWMAtZO2jrMRyFQW9HbwsieQUB07GEUP9qDBIrHUJCRWEnTRmEUFPQgowqz9qiooM24tJBv6CExr8ePsZQEisdQzYXEzjwyBRlOFPzDqD1oMy495Bt6yB56/GgPEigeQ5txSazMI/uxUakiLEFGFWQPPbSKR0K+oScsGSUSKB5jNcgA4WtYNI+sh4KunrAU/qmCCuwl1Fb0hMUeJFA8hjbjkoSlUakiLPPIqiB76KHsq4Rih56w2IMEiseEJRWnAhoh6yHf0EP20GNnVVPY2guJVz0kUAglhMWRVEBBRg/5hh6yhySZ5A8g+JtxqcDsYgOAxGtf/BhLTQmUefPm4fTTT0f//v0xZMgQXHnlldi6davuPV1dXZg7dy4GDhyIyspKzJkzB01NTbr37Ny5E5dccgkqKiowZMgQfOtb30JPT4/9vyaAUNCV2LFFR0f4NuOijJIeaisSK3sGAeG1B/mGnrDYw5RAWb58OebOnYuVK1diyZIl6O7uxkUXXYSOjo70e+644w689NJLePbZZ7F8+XLs2bMHs2fPTv88mUzikksuQSKRwNtvv40nn3wSCxYswL333qvurwoQYXEkFVixRVUVf2aMi5QwYcc3WlvV34/X2PGPsNnDypJ8ILz+Qb6hJyz2KDLz5sWLF+u+XrBgAYYMGYK1a9fi3HPPRUtLCx5//HE8/fTTOP/88wEATzzxBE444QSsXLkSZ555Jl599VVs2rQJr732Gurq6nDKKafgBz/4Ab797W/je9/7HkrMWDQEhMWRVGDFFuXlfElyMgm0tOjn2YOOFXtUV/Pnlhb19+M1dtpK2OyhFSjFxcZ/T/gHxQ5pi44OoKeHT4GFBTv28JNv2KpBaelt9bW1tQCAtWvXoru7G9OnT0+/Z8KECRg1ahRWrFgBAFixYgVOPvlk1NXVpd8zY8YMtLa2YuPGjRk/Jx6Po7W1VfcIA1bmkYHwB10ztojFwtsphyXIqIIEm0TYoqhI7pZrhLDbw0ocBcLXXsLSViwLlFQqhdtvvx1nnXUWTjrpJABAY2MjSkpKUFNTo3tvXV0dGhsb0+/RihPxc/GzTMybNw/V1dXpx8iRI63etq+wOo/sR0dSgZVGBYS3Uw5LkFEFCTaJ3bYSNv+wUq9VXMwzsED47GGlaNiPvmFZoMydOxcffPABFi5cqPJ+MnL33XejpaUl/di1a5fjn+kG2jStmYblR0dSAQVdPXY65PZ2mZ0LCyTYJNRW9NDgRk9YpkMtCZTbbrsNixYtwuuvv44RI0akv19fX49EIoHDhw/r3t/U1IT6+vr0e/qu6hFfi/f0pbS0FFVVVbpHGLA7j+wnR1IBBV09lLbWQwJFYrWt+LETUgHFDj1haSumBApjDLfddhuef/55LFu2DGPHjtX9fOrUqSguLsbSpUvT39u6dSt27tyJhoYGAEBDQwM2bNiA5ubm9HuWLFmCqqoqTJw40c7fEjjsziNTB8ShoCspLZXZOLIH+UZf/NgJqYDsoceOQEkk5BSR15iqW547dy6efvppvPjii+jfv3+6ZqS6uhrl5eWorq7GTTfdhDvvvBO1tbWoqqrCv/7rv6KhoQFnnnkmAOCiiy7CxIkT8ZWvfAUPPvggGhsbcc8992Du3LkoNTPPEQKoUekhe+ixY4/mZhKwAPlGX2hwo4cErES7ArKlBRgyRO09WcFUBuXRRx9FS0sLzjvvPAwdOjT9eOaZZ9Lv+elPf4pLL70Uc+bMwbnnnov6+no899xz6Z8XFhZi0aJFKCwsRENDA7785S/jhhtuwP3336/urwoI1CHroaCrh/xDDxXJSsg39JA99FixR2GhFCl+sYepDApjLO97ysrKMH/+fMyfPz/re0aPHo2XX37ZzEeHElL9eijI6CF7SBizJ1C6uvjvh2WbJfINPWQPPXbs0dbmH3vQWTweQo1KD9lDD9lDkkxykQJYLxoOkz3IN/RQ9lVPWAa/JFA8RBQimVnBA4Q3yJA99IQlyKjA6tbuhYXyELQw2cPKPhdAOH0DIMHWl7DYgwSKh1jZXAiQTtTZqd/sLejYtYdfGpUqSLBJrAoUIJydMmUM9JCY1xMW/yCB4iGiAzLbIYc1bW3XHmGyBWBfsPklyKhAu+zR7JkpYRZsdkbIBkoKAwOJeT2UQSFsY9WJiouBigr+OkydUFhUvyqsCja/BRkVaG0Ri5n73TD6h13f6OnhGdiwQNlXPWGJHSRQPMSqEwH+cyQVhKVRqSIsoyAVWLUFQPbQUlkpN4UMkz3sxo4wiVcgPLGDBIqHWFX9gP8cSQUqBEoY09Yk2EjM98WqPWKxcE6JUgZFT1imy0mgeIjVSnzAf46kAruqv7ub73cRFkigSCiDoseOYAtj7KBVTZKeHiCV4q+DnlEigeIhNCrUY9UelZWyLiFM9rC7MsEvQUYF1CHrUSHYyD/0thCdetCxs+LNb/0KCRQPoVGhHqv2KCjw3xbNKqAMikSFmKcOmRNG/7CbfWUMaG9Xe09eoV3xFvTYQQLFQyjI6KFOSKLd2j3oQUYFJOb1UOzQY9UeZWVyaXJYYoewRSwW/CX5JFA8hIKuHgq6Eu0GfHaWXYelaJh8Qw/FDj12iobDZg+tb1hdku8XW5BA8RDKGOihoCtRkaZNpcKXtiaBwiF76LETO8JWo6SiXssv/QoJFA+hIKOH7CGxU+hWXi5Tu2GzB4lXjooO2S+dkAoodkhU2KKjg68G8hoSKB5Cql8PdUISEWQKC/nDDNq9LsLSCVEHpIfsoYfsIVERRwF/xA4SKB5CjUpPmFKTdrFjCyB8/mFnU8Ow+QZAsaMvNLiR2PGN4mKegQX8YQ8SKB5CjUoPBV2JHd8AwmcPO5saClu0twPJpLp78hKKHXqonk8SpthBAsVDqEPWQ0FXQhkUPSraChCeTohih4QxueqN7BGu2EECxUPoLB49FHQlYQoyKrAjXktK+H4XQHjsQW1FYqegHAhfPZ/d2OGnKVESKB5CaWs9lEGRqErT+iHIqIAEmx5axSOxsyQfIN/oi5/sQQLFQ1Slrdva1NyP19CoUKJqFET24JBgk4StrdjNoITNHmES8yRQPMSO0i0tlQ7oB0eySzIpM0FU6BauIKMCu6NCEmwS4RtdXfrOPajYWZIPUOzoi59iBwkUD6FRsoTmkfWEKU2rgjAFXRWomOIBwmEP8g09YYodJFA8JEyOZBeaR9ZDQVcP2UOPHXsUFgKVlfx1GOxhZ7EBEL7BTZimQ0mgeAgFXYk2gyJOFzWDsEVnp/6gvaBC4lUP2UMPFVFL7Cw2AMLnG2HKzJNA8RASKBJtkDF7AidAaeu+hKkDAqit9CVMnZBdVPpGGE7/DpOYJ4HiFYyFypFskUrZDjJFRUC/fvw12SNcHRBSKcoYCFIpgDESbIJk0vYUj7BFTw8vHA40yWSofIMEitswBvzzPwMDByLeweciwuBIljl8GJg0CYmrrgFgvQMCQmKPN98EqquReP4vAKzbo6aGPx8+rOSuvGPePKCsDPE9+wHYbyuBtkdPD3DhhcDIkUgk+FA/0v6xfTswciTi374XgHVbVFYCBb09YaDt8dxzQEUFEivXAgiHb5BAcZvf/x741a+AQ4eQaONSN9IZlDvvBDZuRPyDDwFY74AAfzUsS8TjwPTpQHs74q+9CcC6PbS2CGza+oMPgO9+F+juRnzrDgBq7BFYHn0UWLYMyc/2Ipnk86CRtsfXvgbs3YvE26sBWLdFLCbtceiQmltzncOHgTlzgEQC8SXqYofXkEBxm9/8Jv0y3sV7DquONGAAfw5so4rHgaeeAgAkwFWaHYESeHssX56u8I2DG8KubySTfLfhQPLEE+mXiVaee7cq5gPvGwAf2ED6BhDh2LF3L/DqqwCkPexkXwNvjxdfTL+0GztIoEQVxoB33kl/mUgVAYhwkFm/Pl3RRUEGwJo16ZdCsFm1R3m5/N0w2CPO7AnYwPtGRwewaRMA6RtAhAWb1jdsdsiAvzplS6xenX5pN3YI3+jo8H5FJAkUN9m2jUeEkhKwU6bY7pQDH2REo2pokEGm2PrBQqGxx7RptoNuLBZweySTwLvv8tcK7BFoWwDAunW8QHbYMMSHjk1/28qSfCAEHbIQKNOmabKv1ucyA+8fwh5nnGG7rWiPUfHaP0iguInInkyZgp7jJoD1mj+yQVd0yBdcgES/WgBACbO+93bg7SGCzD/8g9JRYSDtsXUrn5uqqABmzrQ9Kgx8TY5oK6edhsSIYwAAJUVJS0vygRC1lTlz5EAvZn24H2h7dHdzAQsoiR2FhXIVoNf2IIHiJhs28OdTT0ViuBwFRTaDsn49f546FfFBwwEApazT8uUCPSo8cADYvZu/vuoq2SEX9Fi+pPCPQNpDBNwpU4DjjlOWQUkk+GZ+gUPYY+pUxIeNAQCUFtr3jcDGjvfe489nn414/8EAgNKeDsuXC7Q9tm7l9XxVVcD06bbFPOCfwQ0JFDfZto0/jxuH+DApUOwG3UB2QIwBH3/MX48bh8SgYQCA0p4jli8Z6CAjbDF8ODB2LOKFfFOX0s7Dli8ZCnuMHw+MGWNboFRWyoPkgm6PeP0YAEBpLKLZxo4OXiQLABMmIDGgDgBQmrB+rHug7aGJoxg7VrYVZn1TF7/0LSRQ3EQ40rHHIjF0NAAghpSlEzgBvROlUvZvz1UOHOC7ZsViwDHHIF5bDwAo6Y7oKEj4xnHHAbEYEv34H1PSftDyJQNtDyHmjz0WGDNGjgoLrdUoBb4mR2OPRN1IAEAJi+f4hdz4ZYRsiU8+4c8DBgADBiBeMwQAUNJlfRe+QNtD06+guhrxot7BTes+y5f0iz1IoLiFNmNw3HGID+FBphRx2/PIqRTQZn3w4A3ajEFZGeLVYhRkPciEpQMCgHh5DQB7QSY09hg6VI4KW5otXzKwU4AdHUBjI3997LGIDx4BAChNWZ+r8ssI2RJ924qY4jly2PIlw2SPRCkvICk52Gj5kn6xBwkUt9i3j6uIWIyn4USQQdzyRhVlZTLlHbhOSDSq444DACSqeZCxMwoKTYcMIN4bZErb9lu+ZGjsUViIhCiE3L/H8iUDa4++GYMBPNtYmrQ/HdrZqT9JPBBoMwZAOttY2mV9x8rA+gZwVCyNF/dmUNoPWL4kZVCihnCikSOBsjIkep2oBAlgfwQ7oT5BJl7aHwBQmrC+q1hgbQHop3gAJArLAQAlndaDrl+CjGn6ZAwY0+x1YSPoBtY/+vpGOV8HWpLq0h8DboKqKnkoZ+Ds0VfMF1cCAEriEa1B6ZtBiZUBAEo6rP8xfrEHCRS3EKOgY/gSwXiid6tqxKMpUIQ9RKMq4UHGzhSPtkMO3FLSPvaIF/AgU9ppP8h4naY1jbBFbS0wYIBus6hIZpT6+oaoMUCc13JZoKBA7ncROHv07ZCLKwBENIPS0wN8+il/Lfwj1ivmO6zXr/llOrTI24+PEGIJ6Uhee5I+nRUJy0EGCHDD6mMPsWqlJN7ON+myUDms3d69o4Ov3AgE8bjMGIzmxdNx9AqUKBbJ7tzJn3ttoU0SlLQEv/DPNKIDEr7R3bt/khjcDB1q6bIDBvAOyOtOyDTCP8aMAQDEC3sFio0Vb4H1jT17eMArLk77QXrXZRtifvp0XkJw6qlK7tIypjMob775Ji677DIMGzYMsVgML7zwgu7n//iP/4hYLKZ7zJw5U/eegwcP4vrrr0dVVRVqampw0003oT2wB4YY5LPP+PNwvt9H+kjsqGZQhEAZwWtx4gV8SqMUXZb/mIoKubNmoOwhfKOsDBg4EACQiPWuWmmLoHjdtYs/C/GqqZEoPdxk+bKBzSjlGtxELXYwdrR/xMR06GHLqVM/be9uCmGL4cPTRzInGA+CJa3WfeNznwPuuosLFS8xLVA6OjowefJkzJ8/P+t7Zs6cib1796Yfv//973U/v/7667Fx40YsWbIEixYtwptvvolbbrnF/N0HCdEJiQ5ZK1AUZFACFXQZO0qwJZI8Y2In6AZ2Kak24PYWBsRTPMhEckoji0ApRA8KD0ZwVVMWe0RycHPoEHCktzi4N5YmCnqnNFKdfOsCC4gMChCwWNrHNwAgnuw9483GCkC/YHqKZ9asWZg1a1bO95SWlqK+vj7jzzZv3ozFixdj9erVOO200wAAjzzyCC6++GL8+Mc/xrBhw8zeUjAQoyDRIUd5FNTayocqQOaMkk3B1twcMHtkDDK9AkXBslpRk2N1Obvr9LFHpNsKQPbQIuLooEH8REwA8Z7eDlkINu1hMgYR27u3tnJ7DB6s7I6dpU92DdDYw0a20S84UiT7xhtvYMiQIRg/fjxuvfVWHNB0OCtWrEBNTU1anADA9OnTUVBQgFWrVmW8XjweR2trq+4ROBzOoAQyyAwYwOdlIIOu3VFhIOeSMwiURKo3o5RokyNGkwR2e3dhj0xtJWodcjwONPV2NIrtEei20msLQNpDlWA7aL3sy30yxY4ePhIpOUQC5ShmzpyJ3/zmN1i6dCl++MMfYvny5Zg1axaSSb4DZGNjI4YMGaL7naKiItTW1qKxMfPGMvPmzUN1dXX6MVLzzwgEPT1ya2aHMiiBalR9pncAdUGmlp85GCx7ZMqgaFd5WRSw/fsDRb050kDZo28BtSIxH0jf2NO770tZGc8aQJ1A6S13CpY9MnXIigY3wj+CLNgYAxKiiLp9v+Vl6H5BuUC59tprcfnll+Pkk0/GlVdeiUWLFmH16tV44403LF/z7rvvRktLS/qxS/xTgkJTE9/utagI6BVnkQ66fQpkAXX2CHTQ1dgj0StQ7NbkBM4/GHOsKDRwtgD0vtE7R0f2gF7MKxZsNsKP+2SZ/gPsrxD1A47vg3LMMcdg0KBB+Lh3s6H6+no0N+vn1Xt6enDw4MGsdSulpaWoqqrSPQKFCLhDh6aXz6oKMoHskDNkUFQH3UC1y0zzyIqCbuDssX8/0NXFO+Ns9UkWD57SdkCB2SeHOmQ9OdoKCTa9QLHrH37AcYGye/duHDhwAEN712g3NDTg8OHDWLt2bfo9y5YtQyqVwrRp05y+HW/oU38C9AkyNlpEIINMjikeVfYIVJDJZw8bywoCZw8xpTF4cPq8eJ14TaUsHw0hOqDublmj7XuEPRzwjUB2yDkGN6WIAy3WN2sLXCzt7uYrAoCjxDzQ215s2MMPmF7F097ens6GAMD27duxbt061NbWora2Ft///vcxZ84c1NfXY9u2bbjrrrtw3HHHYcaMGQCAE044ATNnzsTNN9+Mxx57DN3d3bjttttw7bXXRmYFD9AnyNhwokAGmQxTPLog09pj+dKByxh0d/NzmgCg1/+TSf4A7AeZwNlDdMiaWCDbSq+TtLTwJRcmqajgZ1fFexMxgdjIL4M9dIItSh0yIGv5MviHqrYSmFja1MRTgYWF6WVHwjcK0YNCpAIvUExnUNasWYMpU6ZgypQpAIA777wTU6ZMwb333ovCwkKsX78el19+OcaNG4ebbroJU6dOxd/+9jeUilPtADz11FOYMGECLrjgAlx88cU4++yz8atf/UrdX+U3MmRQdEGmrc122rq1NUAbDOUrklUQdAMTZERheFGR3KStb5o2SvYQHZBmd9S0QCnqFa4W7RHImhwhUDLZI4qDmxyCLXIZFNFW6uvTm7SlfaOgtzMIuEAxnUE577zzwHJM4L7yyit5r1FbW4unn37a7EcHl3wpfMZ42trCqLCmhgdexnj1eZ8FUv7EhYxS4ILM0KFHBRkggvbIlTEoYkAPbHdCe/cGyB45MgY822h9ywWteE2l0u7nXzo75ZQWCbbc2cbCHiCFwAsUv7tkOMjQIaeDboG9UWFhodzPIBBBN64p3MqWUYpSxiBHhwwAxeiOlj1yZVCKe7OMUeyENPbQtZUjRyynToUtUilbOsc9hG+Ul+s2Y1OdfQ1EHAUytpW0bxTabyt+gASKG+Qqki3p/UZUOiERcEtLZYQEZVAydcglhT2IAdGyR65RYUlv5jYqnRBj+TMogGV1UVoK9ONndAbDHtq2otkWWdUUT2DFaybfKO4tYiOBQuREu69DpimNst6GZmMIE6hOSGsLTZBRnbbu7AzI7qk5gkxJUe8oSFEaPxDkGhUqEPOBaiutrXIX4Uz26D0YM3KDmz6LKVQNbgIlXgHHs41+gASK0xw6xPd1ADLPq5fzfVEi07AyZJOAPmnrjg6++64FqqrSW80EI+jm6JBVTmkEwjeA3KPCst5vRKVDFr5RXZ0+EgLQ2KO8N3xHxT8ytBVA/SqelhbL4cddctVrKRDzfoAEitNoD7cqK0t/Ox1kKtQFmUAE3QzZJEBd2jpwKzVyTmn0fiMqHXIqJVc1ZRoVlkWsQ85QfwJo7NGvd41DVPwjQ1tJJuUCyHQGxeIufOLYECAg293nyqCQQCEMkWGjJUCjdCt687RRyaDks0dpxDqhXFMaYmW+Ilv4fvfUAwdkwadmV2mZbbTvG4HqkDPUnwAae/SzHzuC3laOWvHW3S0z1iYpKpK1t4Hwj1wZlNLe6XMSKERO8s2bilFQVOoM8tmjf6/0j0onlCuDokCgCFsEYvdU0QENHgwUF6e/LbON9qdDA9Uh58ugVKob3AS1rejPnrG/90dgBns9PXIX2UwZlHISKIQRaBSkJ0OQYUzTsKp6e+UoBBntLrIZg4z9jEFFhZyP9r09sohXmW2M2JRGltihUswHKnbkyaAUV5XzF1GYLs+wiyygNnb4ARIoTpNvFBS1jEGGIJNMyumHkureIKNgVZPv7SGCTFERr1HqJd0hi5qLRMJy2joWC5B/5OuQoyrm+8SOtH/0Vyfmfe8bQN5sY6ymd34mCoMb0Vbq6uSqAGinQ+2LeT9AAsVpXJjSCEzQZSxnkAGA0ureQmIFQcb3B3kKW2i2qga0oyAZeNDWZvljAmePfEWhCmwhdk/1Nflih8g2RsE3urpk5Wq2Jej9+/MvLB4mCQTIHvl8Q0yHtrUFoPgsOyRQnCbL0jg5CuoVKDYalRh8+75R5dnXAQBKqnoFio2gGxh75MsYlMXk8tII2+Oo6VAFtkilArBSI8+y2vTgJkq+UVYmt85Gn3otIVCiZI98Yj6VCsiGUJkhgeI0eTMo9kdBYgpy3z6fi2XRqKqq5BaWkLaIxYCiqt4O2YZg09rD1+RL4WtHhYr8w9cYnQ61YYuSEnnkla87oSzZRiDDFE8UfCPLLrKqBUpg7GG0XguwFUu9hgSKk2j3dcgiUFRmDBIJW5dxnnxirRSIVUVwFJTDHqis5F8oCLqBt4cCgQIEpBNqa8uYbQQ09hDToVES89k6ZEViPnCxI5tvlMXkINDXnUJuSKA4idjXIRbjxUwaRM1jeY39oFtRIWcCfN2w8nRAlDHgqJ5XF0E38PYQGYPOTl5ZbZFA+IewRZ9sI6CxhygoV9Ahd3b6fBl6vg5ZsZj3tW8AhgZ7KmKp15BAcRLhRH32dQCkQCmr6Q0yNtNwgWhYWTqgtFgrR7SCTBbBJuwRqbR1loPxAG3GoFR+M+w1W1lsoVuSP8B+fVJlpdxvx9f2yNMhqxLzgcs25hrciFhKUzxERrI4EaDplGvtj4KAgHRCWYKMqOEqL4fSjEFbm36FkO8wItgUChRfB13tLrJ9so1p/+hfzJdkA+EXbHnqcQCgfID9KZ5YLCAZtiyxVHVbCYQtAHOxlDIoREayOBEgHalsoGae0EaFayAaVp4gU1YGJY2qpkZuDeDrTjnLKFkXZBRklALlG4MHa04646T9ozwWnQxbnuwa0Cd22CAQ9jDSISv0jUOHpF72HVl2kQXUx1KvIYHiJFk65FRKpuLKanvTtD09tob7QQ4yqhtVQUEAOuXubnNBJuyFkFkyBoBzGTZfi9cs9hC2KCgAigfY75CBgGTYXBrc1NbKRUK+3byuuZl3IgUFwJAhuh9RBoUwTp55UwAoH6QpgAv73KlLoyAgAAIly1bVgHNTPAcO+HhzshzZRtWdUKAEWw4xr1vxFvbsa5ZDRlWL+cJCufGlb+0hxFp9vW4XWaCPPagGhchJng4ZAMr6FSrZjMv3QVdbBOlwxgAIgGDTjpAL9M0wPf2nqEMWu2P6enMyA9OhqgWbb9sKYCzbKDqgZDLc2deuLpnOyJJRUpkx8L09XGwrXkMCxUnydMiFhb01f1EIull2kQWcmTf1vT1czBiUlMhj5H0v2FywR6AyBkayjUC4i6iz7CILZBFsYZ/ycrGteA0JFCfJMwoq713AE4mgm2UXWSCio6AsRZBAlk4o7MvQjQZdBfbwfQeUY8m1zhaFarKvvo8dWt/Q7CILOBM7AmWPPpBAIYyh3UU2S1qyrHeVYCRWJpjtgI4csbUZFwUZPUG1B2POTfH4dnOyHNlGnS2AaGRfzbaVqEwPuzS48RISKE6h3dehvl73I12jAqKxwZDZIAPY6j1CZ4+Ipq17emRhryp7VFbKlcy+7JSFLWpqZIakl6Nih4JOyPermjxqK770DYAyKIQChBMNGZJ9F9m+AkXBKMi3m5Pl2LROp/rLymRlekRHhZGb8spxZpW2oFyVPWIxnws2ox0QEPkMSsaMQYSzr1QkSxjDaIcMKHGk6mq5yaYvG5bRoBuLKQ26YqsR35Fj3w8nlgr62h779/NUSY4zq4A+560oskdTk63LOIPRDghQ2lYOHZL7M/mKHPVaWbOvNvxDbC3iy7YCUAaFUICZUZCCGpRYTDYsXwZdA9v+qxwVin7Ol7YAXB8F+doewhZ1dVJl96Kt14rFoMweYtbV1/ZwKYMycKBMWgZtcKNrK6Wl0n9sCBRftxXtBo9Ug0JYxkqHbNORfN2wzIwKFTQsYQtfjgrjcTm3YGYUZGMzLuqQ9QSirRjJvipoK9oNScVMm69wOfvq67Yi/kHFxXKDIw2UQSGM4XKaFpANK9BBBlC2ZbUYTPkuVWslyNg8CkF0yEHzjaNWvFEGBQDZQ5BVsCnIvmrXOfiGHBs8JpNyMEY1KERuaFQoybGLLOCMPXw9KsyxrwPQJ+hq94wJa9raQFtRmTEAgi/YVA9ufGuPjg6gpYW/dnFwI6a8fDe4yeEb2vGLyuyrl5BAcQozHbKiHRB9m0HJsa8D4HxGyXedco6iP6CPfxQVScMoSFsfPOjDKS+jBcNA+MU8YLwoFAi/PYQt+vXTF8H24sR0uXZw4zt7GJj+A9QeheAlJFCcwsooKKw1KMIW1dVH7SILOCfYfDsqzOEbyaRMK6sUbAMG+HjKy8PpUN+1FcasFdjbjB2+tUcOWwARnC43IF6LinrbuqKjELyEBIoT5NhFFnB+Htm3jSqDLYAIFg0b6IAA9VNeQRRsTmcMfGeLQ4fkaNfFbKNv7ZFHoDg9uAlS7DiqXkvRUQheQgLFCXLsIgtEME3r8SjIt/bIIV4B9aPkIPpH1iJIm5tx+XaVl7DFwIG9G7/oodihhwSb5Kh6LSDwhbIkUJxAZAwGDz5qF1kgwmlJsxmUCAYZ4RvFxbJQL9SdUE+PvCEzGRTA1lEIvl3lRWJej9kMiqLsaxDtcZQtgMDvhUICxQk8TksePuyzmihK0+oxG2TCLGCbm/mUaGGh3NJUw1EdsqKjEHy7ysujDjmIYh5wbhl6EGPHUW0FoAwKkQGPgsyAATJh46uGZXVUSKMgTpiDrrBFfb0mZSQ5yh6KNuMSHwn41B4eiXnfTnllsAdjOZahhzH7Go/z8gHA9djhFSRQnCDPlEbWDrm721bqQ3uUia+CLk3xSDo7eS8AGB8FhXnvD7MdMhDumhyPpniCOOWlFVKR2LhOxNHSUj4a7QNlUAhjWB0FAeFM41vNKCmyha+mvESQKSsDamqO+rEbUzy+CrpmO2Qg3ALWTluxsRmXb6e8ciyrPeqkayDcKwDzbPBINSiEMcxmDAoLlWzGBfiwYeXZRZYxZ7arBvRTXr4ZFZrZRVYQ5ikeM5vWCcIs2KxmX3t6bM/N+M4ebW2yY82x4i0W06xFUNxWfLWxoYdi3itMC5Q333wTl112GYYNG4ZYLIYXXnhB93PGGO69914MHToU5eXlmD59Oj766CPdew4ePIjrr78eVVVVqKmpwU033YT2gCq8jNhxJEV1FyLOeU6eXWRzpmlt2kI75eUbe/igQ/aNLQBqK30xm0HRbnwYNv/Is8Gj1jfSWl/R4Ka2Vooe32SUrEyHRk2gdHR0YPLkyZg/f37Gnz/44IN4+OGH8dhjj2HVqlXo168fZsyYgS7NBg/XX389Nm7ciCVLlmDRokV48803ccstt1j/K/zGZ5/xZ6OV50B4OyFhi6qqnEEGUD/FA8h/gWjbniPsYTS7BihL0wpbtLTYWqGrFjv2sOkfvvONVCqvgD0qdig6CkH7kb6xR479ggBnO+SCAvmxvrOHB2LeK4rM/sKsWbMwa9asjD9jjOFnP/sZ7rnnHlxxxRUAgN/85jeoq6vDCy+8gGuvvRabN2/G4sWLsXr1apx22mkAgEceeQQXX3wxfvzjH2NYFuMHhp4e6UgjR2Z8i0go6PprRQ1r+HD+LOK+5+zezZ/z2KKwECgp6f2msEVHBw/aBdZnIn1rjxEjMv5Y2MOJNK3QiB0d3EWPP97W5dSQxx5Oinnf+ca+fbxQPhbLuMEjkGMzrs7O8NlD+Ia4sT7k9A0FHfLw4cDOnT60h5WC8qhkUHKxfft2NDY2Yvr06envVVdXY9q0aVixYgUAYMWKFaipqUmLEwCYPn06CgoKsGrVqozXjcfjaG1t1T18S2Mj71SLimTVWR/E6FUnUBQ5ku+CzK5d/DmLQBG2qKjIkKYFbAeaoNnDSfEai/nQHnkESsa2olig7Nnjk8NehW8MHZpxg0cgjz2orSjtkIUOCIo9tLE0TdSmeHLR2DtZVycm/nupq6tL/6yxsRFD+nTcRUVFqK2tTb+nL/PmzUN1dXX6MTLLP8gXCCcaPjzjvg6AbFgZHSlsQcZgBkUXZMrLZdZE0bSG7+yRp0N2Ksj4yj86OuSSayuCzaZviBR+IgHs32/rUmrI0wGlUjJroPMPxVNevvANwF6HLLKvNtAKWF9gJZaSQHGeu+++Gy0tLenHLuG4fkTcW5YOCHC2ExKNqqlJHgfkKXnskdEWCjfj8lWHDNjLoChKWwM+sYewRVUVf2Qgo38o6pBLSuTmtb7ohPK0FW29ltMZJV9gp60A4RrspVJ5BYqT2TWvUCpQ6nvnTZv6rFNrampK/6y+vh7NfdZ89vT04ODBg+n39KW0tBRVVVW6h2/J40SMOat0hwzhs0uM+aT63EqQAcIZdJPJvPVJTnbIgM9GyXl8A3B+VOirTshgxgBwpkZJ2KK52SdLaw0ObnS+oT0KIUwCZd8+/k+JxbLWoOTMzFMGBRg7dizq6+uxdOnS9PdaW1uxatUqNDQ0AAAaGhpw+PBhrF27Nv2eZcuWIZVKYdq0aSpvxxvyBJmuLjnf7YQjaavPfdGwDKp+nS2AcNbkNDZykVJYmLUI0q0O2ReCLc90FxCxKS+DKXztDCgAZaPkQYNkobovVgEatMdR2dcwTnkJW9TXm6tPilqRbHt7O9atW4d169YB4IWx69atw86dOxGLxXD77bfjP//zP/HnP/8ZGzZswA033IBhw4bhyiuvBACccMIJmDlzJm6++Wa88847+Pvf/47bbrsN1157bfBX8ACGV2kAzo2SfRV084yC3MqgtLb6IMspbDFsWNb6pJxpWptHIQA+9Q2rGZSwTnl5JOa1g3PP7XHkCN8lDTA3pQGEM/tqoK2EsUjW9DLjNWvW4Atf+EL66zvvvBMAcOONN2LBggW466670NHRgVtuuQWHDx/G2WefjcWLF6NMs/bpqaeewm233YYLLrgABQUFmDNnDh5++GEFf44PMDilUVIiz74AEM6g29IiG4aZURCgzB79+/NHWxu3x/jxti5nD6tBpu+qptJSy7fgG98AfBF0fbX3h8diHuD22LHDB/4hbNG/P9+oLQN5Y4cigdLezgc4nlYW+EDMe4FpgXLeeeeB5ViTF4vFcP/99+P+++/P+p7a2lo8/fTTZj86GFiZNwXCmbYW2aQBAzJu0ga4Z48tW3wgUPKkrIEsQaaoiM+td3VxewwcaPkWtKNCm1vM2MfAFI/TS0l901aSSXkTHmUMAB9lDUwsNnDKHv36cW3U0sL/NX4XKDnFvMi+2hjceEEgVvEEhu5uOXlrNWMQpqBrIMiQPfRkTeMr3Gk4FuP7CXq+tDZP0O3ulivRQl8k29Qk65Oy7Jya1zfClH010SEfJVAUHpDnG3tYFfMKj0LwAhIoKtm7l1fAFhfn3aTNqXlkwEeNyqrqByJrD6fT+FrX9Ls9stZrhblDzlGflNU3wthWTGQbnRzc+KYmx2osVXgUgheQQFGJdmvmLLnzvB1QmIKu1SkNQKk9fBNkDNgjMqPk1lb+APJOh+qOQQD0tlC0Gdf+/bbrj+3hg+wa4BPfAHwxxQP4cMrL41jqNiRQVLJzJ3+20wEpDjKebuEt7EFBl2Niyiv0o2Qh1mpq9EXAGrQj5PQxCID+/TZPPaytldPynnZCPumAfCPmTdjDjdgh3NUTUin5D/E4lroNCRSV7NjBn8eMyfoWNyrxRaM6cgQ4fNj25azz6af82WN7iBjn6QbE8bjsAXPYw40gI+wh9KMnCN8YPTrrW7KOkLUbgShYWhs0ezjpG6NG8eddu2wnp+xhxz8U1qAIe3jqG3v38qKxoqKs9UmplDws0MnBjduQQFGJCYHiZM1FRYXcwtvThmXAHm7UoIgYJ2KeJ+zaxdNZ5eXyn5MBNwSbL+xhxze0RyEo6ISCYg83smsjRnDtF4/zHWU9gTF7sTSsbWXkyD57U0iy1msBlEEherETdBVuxgXIhiVuyXV6emTKwk4GRUEHJD5+3z7bMwLW0WaTdPMVEsbcqUER9vDMN7QfbsU3gPB2QgZih5PitbhYZmA98499+/jBQ9r0VgbcsIfWNzybLjfRVoA+xyAAVINC9KJiFASEoxPas4eLlOLirGlJwJ20dU2N3MPAs4ySAd/QHoPg5Cg5aB3yUb4BKLWH521F++F2BjeKOiDP/UPYYtiwnPt2uJltbG31cLrchG8cdQwCQBkUArxnsTOPrHg5mG+CzKhRWZdNAu4EGcAHnZDJUZAbaes9ezw8FI4yKJLDh/luYNqbyUDetpJIKPmHem4PA74BGJgeViDYtNPlfrZH1mwSQDUoBPiEbVdX3rSkW0HX8w7ZgFgD3KlB0d5GEILMUccgAEp9o66Ob0yrPcHddexmUByoQfFcvA4enHXHZcBAWwHCETsMChS3BjdBiB1u9StuQwJFFcKJhg/vs2mDnqyFXYAjaWs/NyrAvbX7oQkyCuwRi8nVCZ7Y48gRWYHpg6ArbsGzlSsGVrsBOewhjkIAwpFRsptBiWD21S0x7zYkUFRhsFEJH3G6E/LNqNBukFGwGZf2NvxsD7dqLgCP/UP0fNXVvEAoC27ZY/hwPguZSACNjbYvZx67bQUIV+wwINiyHoMAhCuDkkza264BoAwKAcNTGsJHhM/ocGBe/eBBj/zSYNDNag/tNxQsvfE0yCQScqOlHPYQfUvGfcscGhV6Yg+THbLTQbeoSO5/5UmnrHJwozj76snKFRNiHnB2HxTAY8G2dy9XYkVFche9DIg/1Y3BjZuQQFGFwSAjdvfOeDKmwiBTVcV3yQT82wnF47Km7yh7KNyMC/A4yBjcA8WQeA3DlJfKthImexgc3GS0h8JOSEz/dXTwAY6rGNwDRfyZJSUZZtQdyr566hs59kAB8vgGZVAI2xkDIDxp/GRS7oGSI+hq/8yjsgaxmNKRkPi37N3rwZkrWt/IsgcK4J54FbeivTVXUdFWHErjkz14OUtdnf7WXGP/fj5fkWexgaG2AgQ/+2rSN9wQ825CAkUV27fzZxVBRpEjeab89+wxlJYUtigvzzI4UBh0Bw2Sq7hd3/Le5AjZTfHqSdA12FZydkKK7eGLUbKPMkqe2cPgHig524pD2dcDBzzo4036hhti3k1IoKggmQQ++YS/Pu64nG+NxKjw44/589ixhtKSGW2h/YECe8RiHmYNtm3jz8cem/NtXvjGrl3cfV1F2GPs2Jxv88IernfIBw/KHcBydEKplOwcQ20Pg76RU6wpzr5qa7n9ag+3pv/chgSKCnbv5sUUxcU505KAu3OFno2ChEDJI9ZyBhkgPFNeH33En+3YQ/FmXMOGce3Y0+PyybWMSf84/vicb3UzbS3aikjuuIbwjWHDcu6Bov0z3cwoeWYPg77hxuAG8NAeBtsKZVCI7AgnOuaYnLum9vTI5WBupPGF6BYi3DUMChS3g4xIYIjbcw2THXLOIAMo6ZQLC6V/uGqP/ft5NI3FeHvJgZtBV/jG9u0uZ5RM+kZhodzyRAe1FT1hs4eKzDzVoEQUg6pf6x9uBF1xOx995PJyQYMZA8NBRlHD0trDNbQZAzv2ULwZF+CRPcSHjRyZpaeVuJm2HjmSrwZJJFw+r8mCb2Sssw5DWwHUZV8djKWu0dYmN+Yx6B9uZF/dhASKCkwGmeLiLPVfioPMMcfwWrG2NpePTjdpj1AHmaYm/v8sKLA3rw4o75Q9sYdB3wDczaAUFspRsieCzWAK3622Mm4cf96+XW6I5gqqBjeK90IR/54PP1RyOWOI1PegQTk3NATytBXFRyG4CQkUFfi0Qy4tlXUXrjUsEzUGORuV9geKg+5HH7m4pbmwxejROY9AANzPKAl7uBp0DbYVxtxPW/vZHoY7ZEVtZdgwvulXMuli3UVLC7BvH3/tswyKNna4hkGxBuTpW7TZ14BN85BAUYHJtKRbHTLgwSh5716gs5MPSe0sqwUcKfwrKuJnOrpWGKoqY6D9QZAzKAYzBp2dUkTmLZJVNH/pqT18FjtiMXlLrtlDtJW6uhx/KMftGhThGzt38vjhCgYHeoA3fYsbkECxSyolU3E+GwUBHowKRTQzkTHIOwpSpPqLiuQsi2tBV9UoCHAs6G7b5mJhqEkxH4vlOUKeMSWbcQEeCJSDB+VWraqyrwpHyK7bQ6WYVxxLhwzhn8WY3FHCcUzYw+3Y4RYkUOyyZw+X1EVF9raqBhwNMq4JFAuNyk3V77pgMzEKclvAjhzJpwG7u11ais6YpRqDjEWhFRVKN+MCPPANMbAZOjTnEmPA27biukAx0VbciqWxmIeDPRUZlIDuhUICxS6iUYn5gxxEKsj4cEoD8GBUaCKDYtgeioKu64WhBw/yOgMg76Z1eW2heDMuQPrGjh0uFYZa6IDcHCH7ua14EUv9mlHq7pbHd7g5+HUDEih2MRFkDDcqhcvBxG19/LFLhaEqR0EOqH5XM0omlhgD3qRpXQ264kNGjJDnDmQhry0A5fZwvTBUZbYx6G0FsDS4CW1baW/n9XyA4WwjQDUoRF+2bOHPIlWRA8NpOEDp7qmiMHT3biWXzI2wh0rB5sBKDVeCTFMT/yMNLDGOx+WoPbRTXuJDVHTIgPJO2fXCUGEPlUWQDrQV1wpDTdjDS8Hmim8IsTZwIDBgQM63Ct/IeqYZQAIlsmzaxJ9PPDHvWw8d4s9Z/c2B5WBFRTKb7ngn1NMDbN3KX5uwR9Yl/g6Ogj75hN+uowjfOOaYvJuSiZkPIMRBV9hj4sS8bxX2cDODArgs2IQ9Tjgh71sNZwy0Stcmgwfzz3OlMHT/frnEePz4vG/P6x9BX4ZuwTfcFPNuQQLFLiaCbl6BAgQ7NbltGw+OFRXAqFF5357XHg7YwtXCUBPiVSzmqK7OcVpC0IuonWorQbRHMgls3sxfm7BHbW2WNziQfY3FXLSH8I0xY/IWDDPmTewQttizx4VSjo0b+bOJgV5W3wCoBiWStLXJfbENKF2vBIpQ/mL2xTG0qr8gv2vltYcDqr+gQNpD9A+OIYKMj8XrhAn8eft2vveIo1jINhoKug7Yw3Hf+PRTPm9SWpr3TCJACtis/qHdntoBe4h/nWOY6JA7O2WJnpsCpbaWZ5UAF2OpgdiR1zcAmuKJJMJL6+ryRFKOoU7IgU75pJP484YNyi6ZGRONqrtbivm8Qaa9XWmFrx/tYcg3RD5b5HQVUFfHd9JmzOFO6MgROU+gKug6YI+TT+bPGzY4fH6V6JAnTMh5wKjAK/8Q9vjgA2WXzIyFDrmoSJ840iFsoZ07VYDWPxzFqdih2B5OQwLFDiacCDDoSNXV/PnwYcu31RfXgq4Je2j/vKw1KNofONQJOYrqjIGwh3izAmIxlwTb1q3c+QYOlMPQHBhqKw7YY8IEnmU7eFAuonAEE74BGPQPYSyF9vB7h5xxjxxA+obCOAq41Fa6umSRrKrYIXxDsT2chgSKHZwQKA440sSJvCHv388XljiGhVFQVVWOAWRZmSwudSDoOjoqbG7mBo/FDBX9GcoYONABAS7ZQ5vCz9qrSLzqkMvLZa2Bo/Yw0VYYM+gfDnTKokPessXhg3At1FwYaivxuNK5S1fayocf8oxxTQ1QX5/37aZ8Q3HscBoSKHYwKVC86oQqKuTySceUfzIpp7xUpSW1b3BAoGzZ4uCGXMI3xo7l/4A8mMoYKB4FuTJKdrKtBHGUbKI+SVtz4bZgGzmSDyJ6ehwslD1wQI6cDNTyCd/IW58k6uCCllGyKOa9GNw4DQkUO5gIuj09sqzEy1GyYw1rxw5Z9Jdnzw/AgkBR2AmNGsXjV3e3XBWtHCeza4cOKZ2rc6VDdsIeDo0KHR8lp1KyCtdExqCwMEfNBeBI7HBlClD4xujRef5AjiHfKChwRNAL921s5AlSR7DYVtwWr25AAsUqR47I7SZV1VwAwRUoolGZLPrLW1vscNB1rBNyssYgmVS6XFDYYu9ePph1BBMpfMDboOt4W9m5k8ePkhJDK3gM1VyIN2h/QRGOCzYnxKv2DQrt0b+/HH/5xR5eTf+5AQkUq2zZwkex2rVnORBtpLKSrwjMSlCDrlNBxuFRsmP2MJHCBwwGmfJyeUK04qA7Zgx/7UjQ7ew0tYIH8DZtLQTbxo0OnfKsXcGT5/wuwOCUBhDcKUCLbcWwPRzyD8ftYVLMG2orHR0uHTSlBhIoVlm3jj9PnqxunlD7Boc6ZMeCrrCHaL158Is9HAkyjMkLqwwysVgwg+7mzXxao7aWr2vOQ2en3FrdC4Fy7LFcC3Z1ObSDqjByADIGgAvZRnFhlW1F+4YgZZQ6O+UKHpVTPGJ1qPYXAgAJFKu8+y5/PvVUQ2/3ulFpg6445V0pwh5Tpxp6u9f2cLRD3rWLz5UUFZkWbIanvII0Sl67lj+feqopMV9YmGf7bm3GQGFNTmGh7BscsYfF2OHFdCggfWP7dgf2+WLMtD0MZRu1bwjS4Gb9ej6CHDKEn15pAEOxtLBQihQSKBHgvff4s8lG5VWQKSyUA5T331d6ab5HiSjxnzLF0K94HWQmTeLPO3Y40F5Fh3zSSXJ3zzx4LdgmT+bPwq2VYkPMG6q5SCSUb4Mr/MMP9vC6rdTW8gOoAZkoVcYnn/DNw0pK1GYMAMfEvGgr77/vwHleWt8wIOZTKe+ny51EuUD53ve+h1gspntMEPslA+jq6sLcuXMxcOBAVFZWYs6cOWhydHMOB0gmZUs1GGSam/nzkCF53uhgtbVIbqxerfjCQvGMGGHgD+QIe+Qt33EoyNTWyvrENWuUXtp0B8SYXBEwcGCeNzvkH6efzp/ff9+B/S5M2kOcGZfXFpWVsiDbIXu8847Sy3I/FilMg2LesG842AEJeyiPHcI3Jk3KU5wnEfbwarA3bhzP7B054sCRCCYz0YcPy022vRJsTuJIBuXEE0/E3r1704+33nor/bM77rgDL730Ep599lksX74ce/bswezZs524Def48EPunRUVho4GBywIFK3nKeKMM/iz8qBrsgMCpD3yliQIe4hhpEIct4eJICNEgVf2GDuWd4CJBM8yK6O7WwpYg/Yw7BuxmGP2EL6xerXi3ZdFSmbsWEPHYwAm7CGu50BbcUywiWyjQd8A5JYpXrWVwkJ5u47Zw+TAt6bGQLJW+IdjS/XU44hAKSoqQn19ffoxaNAgAEBLSwsef/xxPPTQQzj//PMxdepUPPHEE3j77bexcuVKJ27FGUQHdMophpbUAiYalXAixpQrXRF016xRXChrQaAIe+QVbGLY6ECjcnxUaDLIVFXJjXOz0tuWVG/CEIsBp53GXysNups38908q6oMLakFTPgG4Jg9Tj6ZzzocOqS4ZstkBwSYsIewxYEDys+00Ao2pVjINhoWbA75BuBQ7IjHZeWtQXsY7lcAvX8EBEcEykcffYRhw4bhmGOOwfXXX4+dvSf+rl27Ft3d3Zg+fXr6vRMmTMCoUaOwYsWKrNeLx+NobW3VPTzFRsYgb5ApKZEHO4lctyJOOIGfZN7Rofg0TieDjJgDUmwLwKEMyp49fBenggJZyJAHS0HGgaDrSCckfGPKFEMnXAMm7SH8Q7E9SkrkDIwj9rAgUAx3yN3dSs+uAmTG4JNPFJraQoFsS4uJbKNDvgE41FY2buT/u9pavmmdASyJeQdiqVMoFyjTpk3DggULsHjxYjz66KPYvn07zjnnHLS1taGxsRElJSWo6bNTWV1dHRobG7Nec968eaiurk4/Ro4cqfq2zeHklAbgWMMqLHRglHzkiNwDxWCatrWVDxYAE6NCB4LMlCncJnv3Ap99puiiwjdOOMHQFveAPzpkwKE0vsnpLsA/gs1RezghUMrL+QgEUG6PmhpeewEorNnauVOudhNLY/IgbGEq2+hAhyx8Y/16uSTeNiZXuwH+iR1OoVygzJo1C//wD/+ASZMmYcaMGXj55Zdx+PBh/OEPf7B8zbvvvhstLS3px65duxTesUkYM72CB/CP0lWeNdiwgdfK1NUBQ4ca+hUh1iorDfTholEdOcIfCunXT65sUmYPtzpkB4Pu5s0Kl5M62SEDrrQVZaPktja52s1EttEvgk157BC+YWK1m1865FGjeCzv6VG4ssmtthJlgdKXmpoajBs3Dh9//DHq6+uRSCRwuE9tRVNTE+pznNpYWlqKqqoq3cMztm0zvSwOsJhBcbATUhZkhOqfMsUZ1d+/v9w9NQipWgs1Boan/wBHg259PT8cjjH5Z9jCwmo3wKSYdyGj9O67ipaTrlvHjWtitZupbCPgSuxQ1lbcEvNtbdKIiojFAmyPKE/x9KW9vR3btm3D0KFDMXXqVBQXF2Pp0qXpn2/duhU7d+5EQ0OD07eihr//nT+fdprhZXHt7XLw73WQER3y+vWKEhJvv82fzzzT8K+Y6oBiMVeyBkpqtBkDRC2VmEszgJ+CjPAPJfbYsIEXPPXvL+cHDOCXUeG4cXwqobNT0comG75hKNsIuJJBWblSUQ2uBXuYEvPV1XIRg4P2yFE+aZzOTpmZt+AfXot5p1AuUP7t3/4Ny5cvx44dO/D222/jqquuQmFhIa677jpUV1fjpptuwp133onXX38da9euxVe/+lU0NDTgTBMdnKeIJdNnn234V0SjqqgwdFin46nJ4cP5iFBJJ/S3v/FnC/Yw1AEBjtrjnHP484oVCgZZH33EhUNpqXMCRdji0CEHdomS9li+XMHFRFv53OcMr3YzVUANOCrmCwqkWyu1h4m2Yso3AEcF7Kmn8hi2f788LsYy3d0yADllj4ICRwXbuefy5zfeUCDYVq/mNhk61NBp8AK/iHmnUC5Qdu/ejeuuuw7jx4/H1VdfjYEDB2LlypUY3BtIfvrTn+LSSy/FnDlzcO6556K+vh7PPfec6ttwDhtBxmBW19EgE4sB553HX7/+us2L7dzJH4WFwLRphn/NT/aYMIE37q4uYNUqmxcTvjFtmuE5dcBkkNEuQ3dgvwvhG3/7m4IzxYR4FarHAO3tclNYPwTdL3yBP7/xhs0LpVLSP0zYw7RAcVDMl5QAZ53FX9u2x7p1PLs2YICpqXLLgs0Be5x5Jm/me/fysYkttG3F4FQ5YKMmR/EeW06hXKAsXLgQe/bsQTwex+7du7Fw4UIce+yx6Z+XlZVh/vz5OHjwIDo6OvDcc8/lrD/xFfv2yfW5n/uc4V8TK0SGDzf4Cw6OCgEZdG0LFDHdNWWKwdQQR9jDYE2to/bQCjbbQddCNgngq5IBXgOSl6IiKVIcCLonn8wv39Fhsw6FMUtifu9e/tyvn1yQkhOHp7yEbyxfbnPvoM2bedarosLwDrKA9A0/dMiAwrYifOOsswwvPwcs2MPB2FFWJme2ldnDRFsxXUAt9pRKJnkdZQCgs3jMIOotJk40sO+05NNP+fOoUQZ/weG5QhFk3nmHd0SWsdCoAJ50AQwv9XfNHl4EmWQS2L2bvzbsHw52ygUFwOc/z1/bssf27XxPmOJiOVlvAOEbptvKvn3KNycD+F6MVVU8nts6w0qI1zPPNFy7Bliwh4uCzdYg3EJ2DbAQS12yh622kkzKvsWEPfbt45nfWEyelZST0lJ5+mZACmVJoJjBZodsOug6dEbRMcfw1Rrd3bJdWMIte4i5IIfsIYLM22/b2NOgsZEfkx6Lmcqu7dnDS0mKigxmUAD5xhx7B9lBSdAVvnHaaXx/DoOIDsiweBW+kUg4MiosKpJ9hhJ7WOyQDdvDYd847TRZhyK2PzKNxewaYGFw42JbsayP16/ny7WqqgzvBwNI3xg6VC50zIvD9lANCRQzaNOSJjDdIYtjthsbHZkrjMUUTPMcPizPGzdhD8YsBBlhjz17DH+OGcaP5+02HrdRhyJ8Y9Ikeay5AYQtRo40XEfquD1E0H3rLRt1KBY7INMj5PJyeeaKw/awJVAsTv+ZtofDvqGkDuXDD2UxuYkltW1t8tw/v9hDSR2KhWJywIJ4BRy3h2pIoBhFOynvtECpq+MqoqfH8WmNZcssXuDtt7naOO44E0N/vnGkWN5sKC0JON6otHUor71m8SJudUCA4/Y46SQ+g9nRYWOll8UUvmnxCjhuDyHmly+3eNKztpjc5GpFy2K+sVHxgVsS0VaWLLF4AYvF5KKtDBggZyryIgrdHPKNsjJA7JBh2R4WY4fpfgUggRJa3niDDyfHjDF86JnAdJApLpapa2V7sOu58EL+/M47clmnKV55hT+LaGUQYYu6OgNbVQtcaFQzZ/LnRYssXkBEJ1HAYRA/dsgFBcCMGfy1JXvs2sWLyQsKTIt5Pwq2KVN4c2xtlX2JKYTqnTrVVDF5PC6Lhk1NhxYUcHHiUJ3BrFn8+bXX5IorU4i2ItbpGsRWWxGGdABhD0ttJZmUo0STsYMyKITkr3/lzzNnmloGduSITIL4KeiOGMHjJWMWG9bixfxZtE6D2FL9+/ZZHMLm5+KLeVxft473r6b49FO+SqOwUCo/E78K+Ms3AODyy/nzn/9s4ZeFb0ybJlccGcRWJ+SQmC8oAC69lL9+6SULFxCxw2RbEX5YXi7L0vJSVCSXdDjkH6ecwuPHkSMWMrA9PcCrr/LXYlRgEL92yJddxp+XLbNwRMSaNTytXF1tOrvmV3uohASKUUTQNdmoRMCtqjJVmiDXJPuxE/rkEz6PXFQEXHCBqV+11CEPHCirwBwq7ho8WNa2mu6EhG+ceSY/Vc0EfsygANzNi4p4IsT03LrFDjmVkp2y3+whOqE//9lkMWRPj8wY2BDzJsZErkyJCnuYbivvvMMLSWpqTO2dBNgU8wcOKN/uXjBhAp/pTiQsTPOItnLhhbzBmcCvsUMlJFCM8PHH/AyeoiLg/PNN/eonn/BnU04EuDpKfvVVk6la0SF/7nMmVRdffQqYtEcs5vgoGbAh2Cx2yACwYwd/9lsGpbpaZpxNdULd3XJKw6SY37uX/3pBgfwTDeGCmL/wQl4usX27yV1UV6zgq4sGDjS1uzBg0TcAV2PHSy+ZrOMXbeWii0x3yJYEyoABss7FoWkerWCj2KEWEihGEB3y2WebqM7ibN7MnydMMPmZLnTIkydz5+7sNFkcanF6B1BgDxeC7uuvm0jVJhKAOFvKpD16emR2Yvx4E78oCv/a2xUeO3w0loLu22/zexo82NQKDUD6xrHHmuy7XPCNfv1kstCUYNN2yCZWaAByT0gTxxhxXIgd553HbbJnjzzjzhA2OmRL9tAOblzIsP3lLyZqk/ftkycNiqIvE7966BD/8zT7oOZHawsH9g1SDQkUI1ic3gFk0DWxmzPHhUYVi8lO+YUXDP5SPC4nnr2wh4NBd/x44Pjjueb4y18M/tJbb3GhMGQIn5w3wbZtPGPQrx9fZmyYyko+Zwi4klF66y0ThdSiA5oxw9QOoYDcV8OPvgFIe/zpTyZ+yUaH7Gd7lJXJPtWwPZqb5UpIk7EjmZQCxY/2OPtsPmu1f7+JQupXX+UiYdIkE9uMc0QWb+xYg4dICoQtOjoCsZssCZR8tLVZHiEDMsiccILJXxQ9lshrOsQXv8if//hHg6cbL1vGnXvoUJ6CMUFrq6wxMG0Pkcd02B5XX82ff/Mbg78gKoxnzjTdIYsgc8IJpn9V2kPkeR1g7Fh+2nMyCTz9tMFfEvaw0VZOPNHkL4r5ws8+c6yIGgBmz+aZnbVrDW5Stns3r7oGTI+QAQX2cNA3AOCaa/jz735nMGvw8sv8ecoUU1sTAHxqrauLCyMTZ+lxXLBHcTEwZw5/bTh2iFGQm+K1okLuruuwf6iABEo+/vxn3jKOP97ULn8AF8ciY2C6QxZLmbdvdzQVd845vMG3thrMojzzDH+ePdtk5Z4cAdXXm17cIe0hinoc4oYb+PMrrxiYsk6lgD/8gb+ePdv0Z1kOMoBr9rjxRv785JMG3vzBB1x1FRfzZVEmsWyPujq+1CWVkpWDDjB4sPyzDNnj2Wf589lnmzgZk9PRIfsP0/bQxg4HufxynjXYvdvgpm0idlx1lenPEr4xYYLpmTLX28qzzxo4QuTIETl3euWVpj/LsngFXLOHCkig5EM0qmuvNd0hNzXxDVcLCizMI48Zwz+vo8PiRiXGKCiQDWvBgjxv7uoCnn+ev772WtOfZVmsAa41qnHj+MZLqRTw1FN53vz3v/NRe3W1pemuIAiUa6/lemPdOr4jd05EW5k1y/RqJsZkRsm0PWIx1zshQ1mD3/+eP1toK1u3cpsMHmxiibFA2OLTT3mhk0OUlcksSl7Btn+/XOIifskEln0DcM03zj6bf1R7uwyTWVm0iMf2MWNMr2YCghE7VEACJReHD8v6EwuNSjjRMceY2JRMUFIip3m2bTP92WYQWYPXXsuzB8grr/BUy/Dhps6bEShpVNu2OV7c9Y//yJ8XLMjzUQsX8uerrjK1I6bAlj1EZZzDQWbgQFkAmLMTYkzaw0KH3NwMHDzItYapgmGBS0H30ku5TfbsyVNYvm0bL4AsKJDzqCaw1SEPHcr9MZm0sKmPOYRg+9Of8tRr//GP/H5OPdXCaC0YGYNYTMbSvIJN21ZMDnwBRYLN4QybCkig5OKFF3gV44knWmoZorr9pJMsfr5LndDYsXxJKWN5GpZoVFdfbaFoAnjvPf5syR6jR8uMksMncV59NY/vGzfKIvuj6OmRKXwLHXI8LjNKfg66gBRsv/tdjq0k1q7ly/HLy6WiMYE4KfiYY0wW/QlcskdJCXDddfz144/neKPIJp1/vtw4zQQiW2XJNwoKZKGGw/Y480w++33kiPyTM2JDvALSHrY65B07HNv+XyAEytKlOfr/lhZZjyOcyQRNTVzQx2IWVkMClEEJDSJFayF7AsiTgi0kGzjarIHD3HQTf54/P8uJvu3tcn2lhSCTTMpzXcTZFaYoK5OV7g43rJoaWSz74x9nedOyZVwoDRpkem8cgPfniQRP35su+gP0QcbhjNLMmXzn0OZm4Le/zfIm0VYuu8zUdu4C0VZMbqYpcXFUePPN/PlPf8riioxJe1jogAA+ewhYyv5zXMwa3HILf/3jH2fZE+Wzz4A33+SvLcTSlhZ5LqklewwbxpVlTw8vmHGQsWP5njmMAQ89lOVNL7zAlf4JJ5iuawSkb5x0kqWmRgIlFGzbJrdk/tKXTP86YwoEisiguCBQrrmGzyg1NmbJojz1FM9eHH88X9phko0beQq4stJGRkk0LMvHhhrnW9/iz3/6U5aP+9Wv+PPVV/MiDZOIIPO5z1nK8MoapbY2xzNKxcXAHXfw1z/6UYZBaFeXXLpgoa0AentYwkXfmDSJi7ZUCvjJTzK8YdUqXjBcWmqpILSry/K5pBIX7XHLLbwMa+vWLHvmPP44D4hnn21h1zk+sGGMd/5iCyBTFBbKUYAL9rjrLv78+ONZmub//i9/vu46S41ftBXbvrF9u43jyt2BBEo2HnuMP8+caXInHM6OHTwVV1xser8qicjfmdq60holJcA3v8lfP/hgn9o6xoBf/IK/vvVWS41KO0I2XYUvEPldF+xx8snAJZfwTuioLMru3XLJ0623Wrq+7SBTViYDjQv2uPlmvinnhx9mWO317LO8CHLkSG40k/T0yOyaZXsI39iyxdHCUMG3v82ff/3rDDXs8+fz52uv5UYzyZo1PLtWV2f6XFKJi22lqgr4l3/hr3/4wz4Jve5u4Je/5K+9aiuAq/a44AIe8zs7gf/5nz4/XLeO/0FFRcD/+3+Wrm/bHiNH8s2XurtdEWx2IIGSic5OHnkA2fJMIpxo6lQLBbICkf7btMnxuVOAt5eBA3nmT5RXAOB/zPr1vL5AFCSYxPYIGZD2EPlehxGd0IIFfZYc/+pX/P9x7rmW0kHa7JqtoCs+2wV79O8PzJ3LXx/VCYko/M//bHr7coDffns77+gsZ9fGjOFBNx7ntTAO8/nP8+mGri7g4Yc1P2hulkvPb7vN0rW1HZCl7Bog28oHH1i8gDm+8Q2eMFq5ss9GZS+8wCuK6+osFQsDigSKi20lFpOx43/+p8+SYyFe58yxlA7q7JS1jZbtUVAg7eGSf1iFBEom/vAHvqRg1ChL+zkAslzD5AnaekTFYFeXK0G3Xz8eaADg/vs1e16JRvWlL1kaEfb0yMVQtuzhcqM6+2wuqBIJbg8A/AsxvWOxA3r3XZ76LS+3kV0DXBdsX/86v+fVqzVZlNWr+QFwJSWyOMMk2qOdLGfXCgpkRakL/qHthB5+WCNg/+//uI+ccYbps3cEr7zCn88+28YNClvs3OnKjqF1dXLs8t3vagSsiB033ywP/DRBW5sU8+ecY+MGXRZss2fzAwQPHtRkYA8dknsXWIwdy5bxxMfw4VyTW8ZFwWYLFkBaWloYANbS0qL+4skkY5MnMwYw9l//ZekSR44w1q8fv8SqVTbv5/TT+YWefdbmhYxx8CBjgwbxj/zhDxljn37KWHEx/8a771q65rJl/NcHDmSsu9vmzfHYx9jhwzYuZJw33uAfF4sxtmYNY+w3v+HfGDaMsUTC0jW/8x1+iTlzbN7cwoX8QmeeafNCxvnud/lHjhrFWEcHY+z66/k3rr/e8jVPPZVf4pe/tHlz//RP/EL33mvzQsZIJhmbNk3z53d1MTZiBP/Gk09aumZTE2MFBfwSn3xi8wbFvfz97zYvZIxdu2Tce/JJxth77/EvCgv5Dy3w9NP8Escfz1gqZePmNm3iF+rXj//jXOCZZ/hHlpX1/i8ffJB/Y9Iky3/MjTfyS8yda/PmfvpTfqErr7R5IfOY6b9JoPTl+ef5P65/f8b277d0iRde4JcYOdJmo2KMsZtu4hf793+3eSHjPPEE/8iKCsZ2XncX/+ILX7B8vX/5F36Jm25ScHMi6L7xhoKLGeNLX+IfecbpSZY89nj+xX//t6VrpVKMHXssv8TChTZvTATdigqbys847e1cnACM/fs/75O96erVlq63bZvsw5qbbd7cz37GL3bZZTYvZJw1a7h4BRh74xvP8RdDhzLW2Wnpeo8+yi9x2mkKbm7WLH6xRx5RcDFjPPAA/8ghQxg7NOs6/sW111q+3lVX8Ut897s2b6y7mysFgLGtW21ezBipFGPnn88/8vKLu+XI79e/tnS9eJyxmhpF4W/pUn6h0aNtXsg8JFCskkqxJcf+M2tFpa0W8ZWv8P/9N76h4J5++Ut+sfPPV3AxYySTjJ11Fv/Y2bE/sRTA2FtvWb7W0KH8Wi+/rODm5szhF5s3T8HFjPHZZ1yvAow9hlsYGzyYsbY2S9d69105qrJ4CUkyyVhVla3slhWe6+2HSwoSbDPGM3bFFZavJTq0Cy5QcGMrVvCLDRqkYGRgnK99jX/siUWbWRdKGJs/3/K1RIf2wx8quLHvf59f7EtfUnAxY8TjjI0f3zvKxyNcwG7ZYulabW1SU7z3noKbE0FtwQIFFzPGpk2MFRXxj30Rl/FUkMXBxF/+wq9TX89YT4/NG2tpkYOLzz6zeTGzH00CxRI//Mp6nq4t+j1L7bOWPWlslI3KYp+uZ8MGmZp0aZTMGGPvv89YYayHAYw9OvHnlq/zpz/x26+p4cHLNj/5ieujZMYYe+jBbgYwVo4O9v43raXvGZOd2VVXKbqxGTP4Bf/nfxRdMD+pFGMzz2phAGMnYT1rX7nB0nW6u3m8Bhh77DEFN9bVxVhpKb/ghx8quKAxDhxgbFC/DgYw9rXK31p29K1bZZ+xbZuCG1uyhF9szBgFFzP/sQBjf/qC9ezNL37Br3HccYr05re+xS94yy0KLmacu77eyQDGarGf7fj5C5avc+WViqZ3BKKU4Y9/VHRBY5BAscib3/ozKwTviH78Y2vXEG1g2jRFjSqZlHm9tWsVXNAgq1ezH8buYgBjxUVJtmKF+UukUrIN3HOPovtauVIWtLg4Su6Z9yC7CIt5wDw2yQ4dMn+NnTtlOY+yGSoxSr7uOkUXNEAqxfacczWrx570AN3Kv+K3v5X/ytZWRfcmRslPPKHoggZobmZ/rfwiiyFpp/yEffnLirV3a6s3o+TFi9k38SM+U16ZtJRA6eriU+RKZ6jE9P2JJyq6oDE6v34Xm4rVvVN3KUuzf6KcJxZjbONGRTd26638onfcoeiCxiCBYoOH/7uVAbxd//735n63qUkWiS1apPCmLr6YX/TBBxVeNAeJBGOTJ7MUwOaMXJlOK27aZO4yYiqgstJyOc/RxOO85gJgbN06RRfNw0cfMVZWxvajlo0eyP3jvPPMT9GI7ImNcp6jEXPJdXWuFf+JIqU3Sy5ghYWpdImUGZHS3c3YuHEOzNZ9+9v8ol/+ssKL5uE6Xmvxvbr5DOBJnMWLzV1iyxapJdasUXhvogL5N79ReNEctLUxNmoU60YhO3f4RwzgNVc7d5q7jMieDBtmuZznaJqbZWpnzx5FF83DO+8wVlDAdmAUq+0f59Pms80n2UT2ROk4RFQgT5qk8KL5IYFig1SKZwCFSDE6EEsmZbZ96lTFg/tHHuEXPvdchRfNgSgMqK1lrdua2aRJ/MvBg/nUjxH27OF9JsBXrSjl8sv5hX/wA8UXzkAqxRVFb6HE2jWpdD3K2WfzqVwjLF6sKaZUlT1hjEe6ykp+4XfeUXjhLDQ2MjZgAP+8Bx5g8+fLmH/XXcb9XmQalWZPGGNs+fK079qfqDfAokXpYJFctTpd1FlSwthLLxm7RFcXL4oFGLv0UsX3d889/MJXX634wln4+tfTxZd7P25nY8fyL8eOZWz7dmOX2LpVllY9/LDi+zvjDH7h//s/xRfOQDzO2Mkn88+7/nq2ZAn3C4CXbXV1GbuMyDTGYuYHiTnZv1+q4k8/VXjh3JBAsUkyydjNN8vA+/Wv86XD2Uil5NLR8nLG1q9XfEM7dkjFpCwVkYWNG2URTW+uev9+ORCrrOSiLVdH1NLC2Dnn8PefdFLvclSV/OpXvctqzlB84Qw89pj8x/YWBqxcyVh1Nf/2uHH5R7ybN/P+0rHpb1E4/B//4cDFNaRSfPgHMDZlSrom6uGHZVu5/PL8q3F+9zv5fuXT393dUkD97W+KL96HgwflqrJ/+zfGGO+ThIkKChi7777cpWPd3Yx99atSUxntxA0jpkSrqhQVgeXgb3+TKvyVVxhjPHMiVq0NHMhXOOZi3z4eM8QAwOJK/uzcf79UCE5z332yaHvfPsYYH6iI8Hrqqfnrh1eu5KEHcGj1/Nln84vbKOw2CwkUBaRScpQnOqInnji6je/YwdgXvyjfZ3EFWX5EGkNJNWEWDh7kFWkAYxddpFMhhw4x9vnPy7/zoot4JkArVFIpxt5+m7EJE6SYsVjAn5vPPpOB8OOPHfiAXt5+Ww55fvIT3Y/WrOHpZ4BX6d9669GFjfE413giwTF1qsJ0tZYnn1RcTZiFefPkH9ynHup//1eaasgQxn70o6OzSwcP8n5c+NCddzp0n2JvlltvdegDGFcWF17I0nMYGhWeSEjRATB2yil8G6O+CZ0PP5SrdmIxxv76VwfuM5nk87MAr8Fwil27ZMr0xht1P9q9m9tA2GPOnKNXpSeTXNMIvVdf79AszLp1LD0Pd+CAAx/Qy5//LGPU00/rfrR0qRywlJfzfqZvidCRI1z4izY1Y4ZDCUGxN8tZZzlw8cyQQFHIyy/L9i063c9/ns8JNjRIHywudlY7sIcekrLbCbq7ueroTc8Kxa+lp4dv/yGWzYlAcumlvLBPCBOAseHDufp3jJkzHZo/6kUbcGfPzljfceCAXpwC3AazZ/MtKMS2BwCvWWlqcuZWWVubXAe9bJkzn/HSS9LZf/GLjG9Zt46xiRPl31xczIvFZ8/mHbEItkKcOLYo7bXXZNagvd2Zz7j9dv4ZFRVZa6GeekpOVQA863bhhXzwLqZ0AF63ZntPnFzc1buX0cUXO3P9jg6uvgE+kMpQnNXVpRenAC+Cvfxyxi65hKWnggC+suuDD5y5VZZKSbX0s5858xkbN8r2+LWvZXzLZ58xNn26/JtjMZ45mj2bixGRoQV4X+NYV7dnD9+ECFBYfZsbEiiKOXSI70sgRsx9H1/4ArO0ysUU+/fLJZRK1i9r6OmRWxTmCLiCjz7i7U6kKrWPsjLGbriBlyo4iqjAHTxYfSfU1CTnjrMEXEEqxTWBqD/q+6iv51llx1eI//M/8w9Utn5Zw5tvyjRQloAriMd5FvGEEzLbY9Ik/q9zlGSSsWOO4R/46KPqry9GnQbmqJqb+cybmHXSPgoKuJB1vF/48EPZC6repKyzk49QxFRGnjmqDRv4PlHaQY549O/Pk16Oh3VRODVunPqG+cknfIAH8JFsjmm1VIrrfjHL0vcxahRjP/+5C4sVr7jCUNtWhZn+O8YYY25ura+C1tZWVFdXo6WlBVVVVa59bjIJbN7Mjx/p6uLH0px+uqXDjq1x8838rI9zzgGWL7dxkpiGnh7gxhuBp5/mB6E8+6zhI+LFwVXvv8+P2Rg4EPjCF4CaGvu3lZfubn7a8yefAP/5n8C//7ua6+7Zw48j3bKFH+b197/Lo9rzsH8/sGoVP8W8Xz9+aOh551k6P888mzbx80ZSKX4TZ5yh5rqvvw5ceilw5Ai3y8svGzpThTFuh5UrgcOH+WGDkyYBkyerua28PPIIPzxo2DB+YmtFhf1rMsZ97d57+dcm/K6nhx97sno1v0x1NT9rctgw+7dliMsuAxYtAq65Bli4UM01jxzhseLVV/mJqK+8wv8oA7S3c1ts2sR/dfBgYPp0Nf+mvLS28nPODhwAHn8c+Kd/UnPdjz4Czj+fn3Z+3HH8EKHBgw39amMjsGIF8NlnQGUl71POOosfMeU4y5fLQLVli+Mdmqn+23G55ABuZ1B8w65dMm3xu9/Zv15jI597EHUFLm/YYxuxTK6yUk0tyooVfG4K4JPhLm72pYR//Ed+76efbr+6MJXi2QexccuMGbkrxf1GVxffoAzgUxx2OXJE2hdwZwWZSt5/X07Rvfqq/et98omsnK+ocG5q0SnEho91dWrmXv/6V1lYcsIJ7i1jVoWYMp850/GUDU3xhBlRhd6/v72J2hdflHNWlZXG10T6iWRSLhc67TTre8fH47y4RnTG48crOKnNA3bvlnMJX/+69UDT2MjYNdfIzviLX3SoutdhxMZcsRgvWrTK6tWySL2ggB+0FkTEoVj19daXlaZSfN2r8LNBg9RPObtBZ6csmLroIusrnNrbeZWrEH+nn+5gsZmDbNwoSwgcPkaEBEqY6e7m+6EAfLmE2eOS332XV7Rqqzo3b3bmXt3g009lsDznHHPBIZnkWSNtZecXv6h4Yw6XEWcLAHyHSDNz7G1tvL5CVOgVFPCvXdyxVzlid7zSUvMngm/fzvcbEJ3P4MF8CUZQ6ejgu6gCvEbH7KYab76pX8p3xhnmd2DzE+vXy4z0ZZeZOyE9keBn+oh6E4DXgRnd3MSPaDc1+u//dmzjRxIoYefAAVmJXlTEVxTs2JH9/Y2NfI202HBM/N53vuPAJiUesGqVXC4xZAg/lyabb6RSXJD913/JrUxF5/Pb3wa7MxZoNyY59VS+mVg2oZJI8P0r5s7VV3KeeqrlE4p9RSIhj8QF+Mm6uYrAW1p4Fe/s2fpKzuuvd6Hy2wV27ZIFxOXlvII313TErl2845o2TV8J/9//7fy+Km6weLFcXjZ6NI+T2WJiMsmnyu65Rx7pLapZ7WTo/EIqxQ/JFX/X5z+v6JRGPVQkGwVaWnjR7LPPyu+deCJw0km8WrWnh1debdkCfPihfE9BAXDttcA99wAnnOD+fTvFpk3A1VcDGzfyr0tLgalTebFa//68onfnTuCDD7hdBNXVvJjyjjt41XNY+OMfgZtu4gWBAFBbyyu6R47kRa6trbzA+P33gY4O+XvHHw9897vAV77Ci6bDQHc38B//Afzwh/J7Y8cCp5wC1NXxr/fv50WOGzbwQmPBhRfy3z3nHFdv2VGamoAvfxl47TX+dUEBr2A+4QTeBrq7ebXm5s280llQUgJ89avAd74DjBnjya07wqpVvHj400/51xUVvK2MHcur3Ts6+M/Wr+eFtYIhQ4BvfhOYO5e/LwwwBvzqV8Dtt/OVIBdeyIugFUJFslEhleK7G51/vkxDZ3tMnszYf/6nq1sau048zrMn2g1ZMj1KS/kmBL/+dbCnc/LR1MQ3HNFuyJLpMXAgP/Fv8WJ3tof3ivfe41N42g1ZMj2OOYZv2mH0XIcgkkrx6c0zz8xti4ICPpXzk58wtnev13ftHO3t/IgPUVid7VFRwTdu+f3vg1U0bpbt2/nBPw6cd0YZlCjS3MzXdG7bBhw6BBQX80zKccfxTMLAgV7foXswxkfDq1fzJX/t7Xwt4/DhPEMwdSr/Oip0dwPvvcezJY2NfL18RQUfBU+cyB+urGf0Ca2tfNS8ZQsfERcU8EzaMcfwrMrIkV7fobt89hmPHZ9+yteEFxfzzNJxxwGnnQZEKcYyxrOxa9dyu3R28lgxahQwbhwwZQq3D2EZM/03CRSCIAiCIFzBTP/t6bBp/vz5GDNmDMrKyjBt2jS88847Xt4OQRAEQRA+wTOB8swzz+DOO+/Efffdh3fffReTJ0/GjBkz0Nzc7NUtEQRBEAThEzwTKA899BBuvvlmfPWrX8XEiRPx2GOPoaKiAr/+9a+9uiWCIAiCIHyCJwIlkUhg7dq1mD59uryRggJMnz4dK1as8OKWCIIgCILwEW4cY3YU+/fvRzKZRJ3Yg6CXuro6bNmy5aj3x+NxxOPx9NetYm8HgiAIgiBCSSDWFs6bNw/V1dXpx8ioLQMkCIIgiIjhiUAZNGgQCgsL0dTUpPt+U1MT6uvrj3r/3XffjZaWlvRj165dbt0qQRAEQRAe4IlAKSkpwdSpU7F06dL091KpFJYuXYqGhoaj3l9aWoqqqirdgyAIgiCI8OJJDQoA3Hnnnbjxxhtx2mmn4YwzzsDPfvYzdHR04Ktf/apXt0QQBEEQhE/wTKBcc8012LdvH+699140NjbilFNOweLFi48qnCUIgiAIInrQVvcEQRAEQbhCYLa6JwiCIAiCyAQJFIIgCIIgfIdnNSh2ELNStGEbQRAEQQQH0W8bqS4JpEBpa2sDANqwjSAIgiACSFtbG6qrq3O+J5BFsqlUCnv27EH//v0Ri8WUXru1tRUjR47Erl27qADXQcjO7kB2dgeyszuQnd3DKVszxtDW1oZhw4ahoCB3lUkgMygFBQUYMWKEo59BG8K5A9nZHcjO7kB2dgeys3s4Yet8mRMBFckSBEEQBOE7SKAQBEEQBOE7SKD0obS0FPfddx9KS0u9vpVQQ3Z2B7KzO5Cd3YHs7B5+sHUgi2QJgiAIggg3lEEhCIIgCMJ3kEAhCIIgCMJ3kEAhCIIgCMJ3kEAhCIIgCMJ3kEDRMH/+fIwZMwZlZWWYNm0a3nnnHa9vKfC8+eabuOyyyzBs2DDEYjG88MILup8zxnDvvfdi6NChKC8vx/Tp0/HRRx95c7MBZd68eTj99NPRv39/DBkyBFdeeSW2bt2qe09XVxfmzp2LgQMHorKyEnPmzEFTU5NHdxxcHn30UUyaNCm9eVVDQwP++te/pn9OdlbPAw88gFgshttvvz39PbKzGr73ve8hFovpHhMmTEj/3Gs7k0Dp5ZlnnsGdd96J++67D++++y4mT56MGTNmoLm52etbCzQdHR2YPHky5s+fn/HnDz74IB5++GE89thjWLVqFfr164cZM2agq6vL5TsNLsuXL8fcuXOxcuVKLFmyBN3d3bjooovQ0dGRfs8dd9yBl156Cc8++yyWL1+OPXv2YPbs2R7edTAZMWIEHnjgAaxduxZr1qzB+eefjyuuuAIbN24EQHZWzerVq/HLX/4SkyZN0n2f7KyOE088EXv37k0/3nrrrfTPPLczIxhjjJ1xxhls7ty56a+TySQbNmwYmzdvnod3FS4AsOeffz79dSqVYvX19exHP/pR+nuHDx9mpaWl7Pe//70HdxgOmpubGQC2fPlyxhi3aXFxMXv22WfT79m8eTMDwFasWOHVbYaGAQMGsP/7v/8jOyumra2NHX/88WzJkiXs85//PPvGN77BGCN/Vsl9993HJk+enPFnfrAzZVAAJBIJrF27FtOnT09/r6CgANOnT8eKFSs8vLNws337djQ2NursXl1djWnTppHdbdDS0gIAqK2tBQCsXbsW3d3dOjtPmDABo0aNIjvbIJlMYuHChejo6EBDQwPZWTFz587FJZdcorMnQP6smo8++gjDhg3DMcccg+uvvx47d+4E4A87B/KwQNXs378fyWQSdXV1uu/X1dVhy5YtHt1V+GlsbASAjHYXPyPMkUqlcPvtt+Oss87CSSedBIDbuaSkBDU1Nbr3kp2tsWHDBjQ0NKCrqwuVlZV4/vnnMXHiRKxbt47srIiFCxfi3XffxerVq4/6GfmzOqZNm4YFCxZg/Pjx2Lt3L77//e/jnHPOwQcffOALO5NAIYgQMXfuXHzwwQe6eWRCLePHj8e6devQ0tKCP/7xj7jxxhuxfPlyr28rNOzatQvf+MY3sGTJEpSVlXl9O6Fm1qxZ6deTJk3CtGnTMHr0aPzhD39AeXm5h3fGoSkeAIMGDUJhYeFR1clNTU2or6/36K7Cj7At2V0Nt912GxYtWoTXX38dI0aMSH+/vr4eiUQChw8f1r2f7GyNkpISHHfccZg6dSrmzZuHyZMn4+c//znZWRFr165Fc3MzTj31VBQVFaGoqAjLly/Hww8/jKKiItTV1ZGdHaKmpgbjxo3Dxx9/7At/JoECHnCmTp2KpUuXpr+XSqWwdOlSNDQ0eHhn4Wbs2LGor6/X2b21tRWrVq0iu5uAMYbbbrsNzz//PJYtW4axY8fqfj516lQUFxfr7Lx161bs3LmT7KyAVCqFeDxOdlbEBRdcgA0bNmDdunXpx2mnnYbrr78+/Zrs7Azt7e3Ytm0bhg4d6g9/dqUUNwAsXLiQlZaWsgULFrBNmzaxW265hdXU1LDGxkavby3QtLW1sffee4+99957DAB76KGH2Hvvvcc+/fRTxhhjDzzwAKupqWEvvvgiW79+PbviiivY2LFjWWdnp8d3HhxuvfVWVl1dzd544w22d+/e9OPIkSPp93zta19jo0aNYsuWLWNr1qxhDQ0NrKGhwcO7Dibf+c532PLly9n27dvZ+vXr2Xe+8x0Wi8XYq6++yhgjOzuFdhUPY2RnVXzzm99kb7zxBtu+fTv7+9//zqZPn84GDRrEmpubGWPe25kEioZHHnmEjRo1ipWUlLAzzjiDrVy50utbCjyvv/46A3DU48Ybb2SM8aXG//Ef/8Hq6upYaWkpu+CCC9jWrVu9vemAkcm+ANgTTzyRfk9nZyf7l3/5FzZgwABWUVHBrrrqKrZ3717vbjqg/NM//RMbPXo0KykpYYMHD2YXXHBBWpwwRnZ2ir4CheyshmuuuYYNHTqUlZSUsOHDh7NrrrmGffzxx+mfe23nGGOMuZOrIQiCIAiCMAbVoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4TtIoBAEQRAE4Tv+Px2nao4JjjqnAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -95,7 +95,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "39.3 ms ± 543 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "37 ms ± 324 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -128,7 +128,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -177,7 +177,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "361 μs ± 12.8 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "338 μs ± 5.03 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "77897175",
    "metadata": {},
    "outputs": [
@@ -210,7 +210,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "b8cf0127",
    "metadata": {},
    "outputs": [
@@ -263,7 +263,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.25 ms ± 17.2 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "1.19 ms ± 9.88 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "640a92e4",
    "metadata": {},
    "outputs": [],
@@ -295,52 +295,7 @@
    "execution_count": null,
    "id": "8cb48453",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content of stdout:\n",
-      "_cython_magic_79cd32f006e4304f97a9db016a885153f9474399.cpp\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\dense.cpp(119): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(53): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(68): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(98): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(130): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(341): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(184): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(260): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(270): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(276): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(288): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(535): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(50): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(52): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(57): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(75): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(85): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(160): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(226): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(444): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(510): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(574): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(579): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(603): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(621): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(989): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(111): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(265): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(270): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk12b\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(297): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_79cd32f006e4304f97a9db016a885153f9474399.cpp(19659): warning C4244: '=': conversion from 'Py_ssize_t' to 'unsigned int', possible loss of data\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_79cd32f006e4304f97a9db016a885153f9474399.cpp(25086): warning C4551: function call missing argument list\n",
-      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_79cd32f006e4304f97a9db016a885153f9474399.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_79cd32f006e4304f97a9db016a885153f9474399.cp312-win_amd64.exp\n",
-      "Generating code\n",
-      "Finished generating code"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%cython --force \n",
     "# distutils: language = c++\n",
@@ -402,9 +357,10 @@
     "    cdef double* t_span_ptr   = &t_span_arr[0]\n",
     "\n",
     "    # Assume constant args\n",
-    "    cdef double[2] args   = [0.01, 0.02]\n",
+    "    cdef double[2] args       = [0.01, 0.02]\n",
+    "    cdef size_t size_of_args  = sizeof(args)\n",
     "    cdef double* args_dbl_ptr = &args[0]\n",
-    "    # Need to cast the arg double pointer to void\n",
+    "    # Need to recast the arg double pointer to a generic memory holder. CyRK uses char pointers for this purpose.\n",
     "    cdef char* args_ptr = <char*>args_dbl_ptr\n",
     "\n",
     "    # Run the integrator!\n",
@@ -417,6 +373,7 @@
     "        rtol = 1.0e-7,\n",
     "        atol = 1.0e-8,\n",
     "        args_ptr = args_ptr,\n",
+    "        size_of_args = size_of_args,\n",
     "        num_extra = 2\n",
     "    )\n",
     "\n",
@@ -429,28 +386,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "50766a13",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Was Integration was successful? True\n",
-      "Integration completed without issue.\n",
-      "Size of solution:  360\n"
+     "ename": "NameError",
+     "evalue": "name 'run_cysolver' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m initial_conds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray((\u001b[38;5;241m20.\u001b[39m, \u001b[38;5;241m20.\u001b[39m), dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat64, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m time_span \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0.\u001b[39m, \u001b[38;5;241m50.\u001b[39m)\n\u001b[1;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m run_cysolver(time_span, initial_conds)\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWas Integration was successful?\u001b[39m\u001b[38;5;124m\"\u001b[39m, result\u001b[38;5;241m.\u001b[39msuccess)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(result\u001b[38;5;241m.\u001b[39mmessage)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'run_cysolver' is not defined"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -479,18 +428,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "10fc3a1c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "107 μs ± 870 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%timeit run_cysolver(time_span, initial_conds)"
    ]
@@ -506,30 +447,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "506b853f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regular Shape:  (2, 60)\n",
-      "t_eval Shape:  (2, 500)\n",
-      "Dense Shape:  (2, 250)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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MHj0a5513HiZMmIDOnTvj0KFD2LBhA7799lu89dZblmy/8MILWLhwIS688EIce+yxaGlpCVd0HTFiBAASqv/666/jggsuwO9+9zv89Kc/RWZmJnbt2oXPP/8cF110ES655BJdG1dddRW+/fZb/P3vf8f27dvxy1/+Ep06dcKmTZvw5JNP4vvvv8esWbNw3HHHhf/n2muvxYsvvohrrrkGv/rVr3Dw4EE8+uijEcQHIKpT165d8d577+Hcc89FaWkpysvLw+WwAeCdd96Bz+fDyJEjw9XeBg4ciPHjx0fYu//++/HnP/8ZQ4cOxfr16/Hss8+iuLg4wl7//v0BAP/85z9RWFiInJwcdO/ePSq8MC3Bt96CgNtgVNHl6quvlgCEq71JkiQ1NzdLf/zjH6WuXbtKmZmZUlVVlXTzzTdLhw8fjvjfrl27ShdeeGHUMWm1t7feesvUedCKKPv37w9/98EHH0gDBw6UcnJypM6dO0t/+MMfpLlz50ZVORHV3gQE+OHuu++WqqurpYyMDEsViLZt2yb98pe/lDp37ixlZmZKHTt2lIYMGSI99NBDUfvW1dVJubm5EgDppZdeivp7a2urNGnSJKlz585STk6OdNJJJ0nvvvuu5tgAUe1NQCAM+k6mP1lZWVJFRYU0dOhQacqUKdK+ffs0/++7776Txo8fL1VUVEiZmZlSZWWlNHz4cOmFF16IOrb6fU/9AzpWfP3119Ill1wide3aVcrOzpbKysqkoUOHSu+//37E/7W3t0t///vfw35BQUGB1KdPH+mmm26StmzZYqq9H3/8sXTBBRdIZWVlUmZmptS5c2fp2muvldatW6e5/6uvviqdcMIJUk5OjtS3b1/pzTff1BxXFixYIA0aNEjKzs6WAISrs1HfZsWKFdLYsWOlgoICqbCwULrqqqukvXv3RhyjtbVVuuuuu6QuXbpIubm50tChQ6VVq1ZFVXuTJEl66qmnpO7du0ter1cCIE2fPt1U+1MdHklSlOcQEBAQEBAQEBAQEGCGyZMn48EHH8T+/fvD+VICzkFUexMQEBAQEBAQEBAQSAuInB8BAQEBAaYIBoMIBoOG+/h84vUkICAgIGA/RNibgICAgABT0BAPI2zbti0iEVhAQEBAQMAOiLA3AQEBpnj++edx4oknoqioCEVFRTj99NMxd+7c8N8nTJgAj8cT8XPaaadFHKO1tRW33347ysvLkZ+fj3HjxmHXrl2smyIQJ379619j2bJlhj90sUQBAQEBAQE7IZQfAQEBpvjggw/g9XrRs2dPAMCrr76Kxx57DCtXrkS/fv0wYcIE7N27F9OnTw//T1ZWFkpLS8O/33zzzfjggw8wY8YMlJWV4c4778ShQ4ewYsWKiEUtBQQEBAQEBASUEORHQECAO0pLS/HYY4/hhhtuwIQJE3DkyBG8++67mvvW1dWhY8eO+Pe//40rrrgCALBnzx506dIFH3/8Mc477zyGZy4gICAgICCQTEjKjNJgMIg9e/agsLAQHo+H9+kICKQ1JEnC0aNHUV1djYwMa5G0gUAAb731FhobG3H66aeHv//iiy9QUVGBDh06YOjQoXj44YdRUVEBAFixYgXa29sxatSo8P7V1dXo378/lixZYpr8iHFEQMA9SGQc4QUxhggIuAdWxpCkJD90lldAQMA92LlzJ4455hhT+65Zswann346WlpaUFBQgDlz5qBv374AgNGjR+Pyyy9H165dsW3bNtx///0YPnw4VqxYgezsbNTW1iIrKwslJSURx+zUqRNqa2t1bba2tqK1tTX8++7du8M2BQQE3AEr4whvCF9EQMB9MDOGJCX5KSwsBEAaWFRUxPlsBATSG/X19ejSpUv4uTSD448/HqtWrcKRI0fw9ttv47rrrsOiRYvQt2/fcCgbAPTv3x8nn3wyunbtio8++giXXnqp7jElSTKcfZ06dapmhTExjggI8Ec84whvCF9EQMA9sDKGJCX5oQ4OrRYlICDAH1bCPrKyssIFD04++WQsW7YMTz/9NF588cWofauqqtC1a1ds2bIFAFBZWYm2tjYcPnw4Qv3Zt28fhgwZomvz7rvvxh133BH+nQ6UYhwREHAPkil8TPgiAgLug5kxJDkCawUEBFIakiRFhKQpcfDgQezcuRNVVVUAgMGDByMzMxPz588P71NTU4O1a9cakp/s7OywkyKcFQEBAQEBgfREUio/AgICyYt77rkHo0ePRpcuXXD06FHMnj0bX3zxBebNm4eGhgZMnjwZl112GaqqqrB9+3bcc889KC8vxyWXXAIAKC4uxg033IA777wTZWVlKC0txaRJkzBgwACMGDGCc+sEBAQEBAQE3AxBfgQEBJhi7969uPbaa1FTU4Pi4mKceOKJmDdvHkaOHInm5masWbMGr732Go4cOYKqqiqcc845ePPNNyPieJ988kn4fD6MHz8ezc3NOPfcczFjxgyxxo+AgICAgICAIZJynZ/6+noUFxejrq5OhK4ICHBGsj6PyXreAgKpiGR8HpPxnAUEUhVWnkeR8yMgICAgICAgICAgkBYQ5EdAQEBAQEBAQEBAIC0gyI+AgICAgICAgICAQFpAkB8BAQEBAQEBAQEBgbSAID8CAgICAgICAgICAmkBQX4EBAQEBAQEBAQEBNICgvwICITQ2gpcdx0wezbvMxEQSH7wXEShvh44dIiP7e3bgYUL+dheuxZ46CGguZm97VWrgJ/9DNi4kb3tVEdLC/DUU8Af/gBs3cr7bAQEYuPHH4ErrwRGjwYWL+Z9NtEQ5Mel2LEDuPlmYNMm9rbXrweOPx6YOZO9bQBYvRo4eJC93a+/Bl57jTgPPLBtG7n2AqmD/fuBiROBNWvY2z5yBBgxAvjXv9jbbmoiY8jPfsbedjAI9OoFHHMMOQ/W6NEDOPdc4Jtv2NseNAi4/35g6lT2ti+8EHj7beDii9nbTmVIEjBhAvD73wN//ztwyinA5s28z0pAQB+NjcB55wFvvgnMmweMGgWsXMn7rCIhyI8ODh4EhgwBnnuOj/0ZM4AXXgCef5697QULyOD6f//H3vb33wMDB/Jxmhobybahgb1tADjzTPJi42VfwH5MmgQ8/TS5r6zx1FPAZ58Bv/oVe9uffw5s2UKc4bY2trb37gX27SPqB+vJo2CQ/ADkGrCG30+2Cxawt71nD9nymLBLZXzyCXEiASA/n0xq/Pa3XE9JQMAQM2YQBbikhExENTcT1dJNEORHB199RZSAf/6Tj/2jRyO3LEFDJniETmzfTrY//MDedksL2fJod3s7cR6amoADB9jbF3AGS5eSbWsre9vKfhQIsLWtHLd+/JGt7R075M87d7K1vW+f/Jk16VOGGbK2LeAcXniBbH/3OxJamJVFCNGyZVxPS0BAE5IEPPMM+fzXvwKffkr67GefEZ/aLRDkRwft7WRbV8fHPk8CwtM2DVOhRIQlqIPK85rzsi/gDAoK5M+sCYgSu3eztackPNu2sbVNJ1B42Fa2mzXxOnxY/syzrwnYh/37gQ8/JJ9vugno2RO4/HLy+0sv8TsvAQE9rFlDIodyc0kOdbduJPcHAF59leupRUCQHx3wJj88VQhqkwcBoeSHR7tpe3nkCSjby+O6CzgDn0/+zDoBv6ZG/sya/NAQKPVnFti7V/6sVGJYQKm2sb7fyjxJ1kqjurgFDf0TSAwLFhAiO3AgcMIJ5Dsaxjp7Nh9FWUDACHPnku3w4fLk3y9+QbZu6rOC/OiAxk7X1fGpWuQG8pNutuk1DwRk8ssKSsLFo+2BACk0IZwWe6F0SFmHsB45ws82zZ/jYVv5LAnbbKB2aETeoj2guVsjRsjfnXUWUFVF7vEXX3A5LQEBXcybR7bnny9/d845QKdOxJ/+8ks+56WGID86oM5vMMhnIE9XAkJf4H6/TEBZQfkCZ63+8A57mzaNzC7S+HIBe6DsR/X1bG0r+1E6OeI82y1sE7Du66mKzz4jWyX5ycgAxo4ln99/n/05CQjoob1dznNV99kLLiCfP/6Y/XlpQZAfHSgdbx6hb1SF4BEC5QbVSXkerKC0x7rtvMPe6NocYg0Je+EWh1TYZgM3kT6WEQvq8ZJHoZ5Uw969pHiHxwOccUbk38aNI1s6yy4g4AasW0f8l6IioHfvyL9deCHZ0rA43hDkRwfKsCdl+AgruIGA8FR+eNhXKj+sbfMOe6MztW6Jx00VKO9lOik/goDwtR0MslWvBfmxHytWkO3xxwOFhZF/O/tswOslVVFZV1MUENADrUB48slE7VFi+HCy3biRFPLgDUF+dKAkPzyVn3QjP25RftIt7I06K6LYgn0IBiPJZDqRAEFA+F5z1vZF2Jv9+PZbsh08OPpvhYXy2mE81pMSENACJT8//Wn030pKgL59yeclS9idkx4E+dEB77A3NxCQlhb2xR7SVfnhHfZGnRVBfuyD+lryVH6E6sQGatKXLqFnQvmxH1T5Oekk7b+fcw7ZLlrE5nwEBGJh9WqyHTRI++80fPOrr9icjxEE+dGBCHsjYB0GpXQe0kn54R32Rp0VEfZmH3jPhgsCwrfdfj/b58lN5EdZ7U8gPqxZQ7Y/+Yn234cMIdv//Y/J6QgIGEKSgPXryed+/bT3oeRn8WI252QEQX50wFv5cQv54amAsLbtloIHPHN+hPJjH9T3kSWhlqRI+6wrVirbytq2st2snXCe91xtm+WzzNN2KqKlRV6gl67vowYNe1u/XihtAvyxezfph14v0KuX9j5nnkm2K1bwHyME+dGBW3J+eISe8XTE3RL2xjPnh8egIHJ+7AdPh7C9PXLNJtb3lWd/5mlbPW7wJCAsx0+etlMRW7eS57e4GKis1N6nUyega1fiH9AQOQEBXqCqT69eQFaW9j7HHUf6bVsbsHw5u3PTgiA/OlAqPzzC3ujLQ5JIR+FhW/2Zte10KnXNO+xNKD/2Q30tWd5Xnk642j5P2zzHL9b23US8xDiSGOjSA336kFLXeqCJ5SL0TYA3NmwgW1rUQAvKsu28834E+dGBW5QfIL1CsNyi/KQT6Wtvl22KnB/74KYwpHTqz0p7gQDbxZJ53nO1LZ62hfKTGJTkxwiU/HzzjbPnIyAQC5s2kW2sPnvqqWTLW60U5EcHPAseSJJ7HPF0sp2upa6V8eJixtY+8CQgPB1htVrNuj+rlXKWbXeT7XTpb6kI6kgef7zxfkL5EXALvv+ebHv0MN6PVoJbtcrR04kJQX50wLPgAc9ZNHWiNE/lh/ULNF0XORXkxxnwdAh5OsJqpYV1n+LZduWkGWvbPImXut1iHEkMP/xAtrEcyZNOIotJ7toF7Nnj/HkJCOjBbJ+l1Qu3bOFbqEOQHx3wDHvj6TSpE6XTiQS4Rflh7TgoSzALp8U+uMkJTydHmKd9NxGQdCF9qQha6e2444z3KyiQcyx4hxEJpC/8fmD7dvI5Vp/t2BHo3Jl8pusC8YAgPzrgWfCAp/LDO3zBLbkC6RTup5x9ETk/9iFdnXAt2ywrVrqJgPC+7ulgO9XQ3AzU1JDP3bvH3p/OpPN0JAXSGzt3Ep85K0smNkagfXblSkdPyxCC/OjATcpPOpUsTdeCBzzbLZQfZ5CuM/Fq2wBbUu2m0LN0VfuE8hM/6Ax6URFQWhp7/xNPJFtBfgR4geb7dO9OwjBjwQ15P4L86IBnwYN0Xq9BFDxgT0BEzo8zcJMzylMF4G2fx3WnDgAP2z4f2fIgXnR9DzGOxA+aO9G9u3GZawpBfgR4gxJ2M0olICs/gvy4EMqwt8ZGtuVS01X5CQQiHZd0KnjAk/QplR91zpdA/KB9OSeHbHkoEHl57G1TRzgnRyYBPOwXFpItDwJSVMTeNr3nPGzzbHeqgeb7mHUkKfnZvFkobgJ8sHs32XbpYm5/qvysWaMdKcAClsjP888/jxNPPBFFRUUoKirC6aefjrlz54b/LkkSJk+ejOrqauTm5mLYsGFYt25dxDFaW1tx++23o7y8HPn5+Rg3bhx27dplT2tshPqGKB1Ep5Gu5Ie36sRT+XFLtTdA5P3YBZ4OoZZtVnk31AnPypKJH6u2B4NkEgWQ286DdPKw7SbiJZzw+LFzJ9kee6y5/SsrSRJ5MAisX+/ceQkI6IG68MccY27/7t3JWNHWJq9pxRqWyM8xxxyDv/3tb1i+fDmWL1+O4cOH46KLLgoTnEcffRRPPPEEnn32WSxbtgyVlZUYOXIkjiq8q4kTJ2LOnDmYPXs2Fi9ejIaGBowZMwYB+sZyCdRKD8vQt3RdHFFNONIp58ct1d542E9VqBUIno5wMMhuhk0ZApWbSz6zaruyjW4hnaxtu6G/Od3uVJ6IpbPoZhLHARIaJ0LfBHiCPjZW+mz//uSz6rFkBkvkZ+zYsbjgggvQu3dv9O7dGw8//DAKCgqwdOlSSJKEp556Cvfeey8uvfRS9O/fH6+++iqampowa9YsAEBdXR1efvllPP744xgxYgQGDRqEmTNnYs2aNViwYIEjDYwXakeBZdEDofwQsHQcgkG+CzO6SfkRjos94BmGpLbN0j4dOzMz2Ss/ymfYDcpPuoW9sSJeqTwRa5X8ADL5+e47+89HQCAWaJ81q/wAQL9+ZMtLrYw75ycQCGD27NlobGzE6aefjm3btqG2thajRo0K75OdnY2hQ4diyZIlAIAVK1agvb09Yp/q6mr0798/vI9boFZ+0pX8sHyB8lR+1KFePAse8Mz5AZy/56nsuCihng3nMROfny9/x5qA8Ah7c4vy4wb1JZVJXypPxCZCfoTyI8ADVpUfQF6fKimUHwBYs2YNCgoKkJ2djd/85jeYM2cO+vbti9raWgBAp06dIvbv1KlT+G+1tbXIyspCSUmJ7j5aaG1tRX19fcSP01ArPyzD3txEfniGvbF0WtTkJ53C3ljn/KSy46KEG/JusrPZF1xQKj+sw95ouz0emfilm/riBttsC+Wwm4h12heRJEF+BJILTU3A4cPksxXlh5KfpFF+jj/+eKxatQpLly7FzTffjOuuuw7rFWfvUdVmlCQp6js1Yu0zdepUFBcXh3+6mC0pkQB4hr25KfQsXWyrHQWeBQ9aW9lWXOOZ85NKjosabsi7URKQdFJ+eBAvpf10UF+U4FFhj8dErNO+SF2d/C6wQn769CGE/+BBYP9+W09JQMAQlKzn5wPFxeb/j4a9bdmivTyC07BMfrKystCzZ0+cfPLJmDp1KgYOHIinn34alZWVABA1cOzbty88CFVWVqKtrQ2HKU3U2EcLd999N+rq6sI/O2k5FAdBw97o7KFQfpyHm8LeWNoOBqPts3RcWOf8AKnpuKjhtrwbHsoPr5wfHsRLaZ9nmW03hNyxaDePiVinfRHqSHboIJepN4O8PKBbN/JZVHwTYAllyJuZdakoqqvJeBEIkDLtrJHwOj+SJKG1tRXdu3dHZWUl5s+fH/5bW1sbFi1ahCFDhgAABg8ejMzMzIh9ampqsHbt2vA+WsjOzg4nR9Mfp0EH8/JysuWZ85Mu1d545hvxJJxa7WTZdrUAwqLUdSo6Lmpo5d2wDv/iQQKUtnlVe+OheAUCclijG0LPUr3UNY+JWKd9kXhC3ihOOIFsN2yw73wEBGIhnmIHACFKPIseWCI/99xzD7788kts374da9aswb333osvvvgCP//5z+HxeDBx4kRMmTIFc+bMwdq1azFhwgTk5eXh6quvBgAUFxfjhhtuwJ133onPPvsMK1euxDXXXIMBAwZgxIgRjjQwXlDlxw3kh4cjnpnJ3jZVfugq4elS8EBpi8eikDyUn1R0XNRQlnxOp/Avtyk/rAknkL5hb9R2IMB2YXCAzUSs0xDkRyDZYHWNHyV4Fj3wWdl57969uPbaa1FTU4Pi4mKceOKJmDdvHkaOHAkAuOuuu9Dc3IxbbrkFhw8fxqmnnopPP/0UhVSHB/Dkk0/C5/Nh/PjxaG5uxrnnnosZM2bA6/Xa27IEoVZ+eK7zw0N9KS0F9u7lY7usDKip4aP8FBQADQ3k/vv9gM/SExIfaLuzsshPQwMf5Sc/H2hs5LPOj5bjMii0DDR1XB555BEAkY7L+PHjAciOy6OPPsr+5HWgVkBaWtgTEN7EKx1tA+kb9qZ41aO5OfJ3O3HPPfdg9OjR6NKlC44ePYrZs2fjiy++wLx58yImYnv16oVevXphypQpuhOxZWVlKC0txaRJk7hPxAryI5BsSKTP8ix6YMm1e/nllw3/7vF4MHnyZEyePFl3n5ycHEybNg3Tpk2zYpo53BD2VlREHFOe5IdHqevSUkJ+eCheJSWEfADOvryVoO3MzZXJDw/lp6IC2LbN+Xueqo6LGm5QQHgXPOBV7U1pm+caQ+lWaU45Xra0ODd+pupErCA/AsmGRJQfnmFvDOa1kxNuCHsrLeVLfpS/s4CS/LC2TcPeOnQAaCoIK/JD252byz7csL1d7m8dOxLy43TOT6o6Lmq4LfQsHcLe3NBuj4coyCxtK+3zDLnLziY/ra3O2k/Vidh48ycAmfzs2kX8Bgap0QICtig/mzeTMYSmPLCAID86oC8SWlBKnRfhJOhLo6QE2L6dT8EDHgREbZuui2Klgki8oNc4N5c4TSxDlKidvDw5zI7VPVf2a0r0nbadqo6LGm4pOuAGAsLats+XXraV9nmG3NF73trKJ3w22ZGII1lSAlRWArW1wMaNwE9/au+5CQhoIRHl55hjyHh19CiwdatMhlgg4WpvqQqq/NAZPBYVsCiUIVhA+ik/ZWVky3JdFHp/s7PlWXpWRQ+UYW+sFQKa75OTw8dpSmXwVH7o+OXzpZftQIBseVV7A/iRH/U7y+9nV3RAec9ZK26phETIDyBC3wTYwu8nZBuIr896PPzyfgT50YG6TG26kR83EC+W9uk1z8mR11dgZVsZ9sbacaDKT1ERH4ctlaHlELK6tjydUS0SwJp4eb38bPMgfYB83Sn5Afj2N5bvzFRAezuwbx/5LMiPQDJg/34SnZORQcLm4wGvim+C/OhAKD/sbVMS0KGDHOrG6uXtBuUnL4/9bDVVfgoLSdsB4bTYBaVDyPraKgkIa9taBCQd2s3TtiQRpRyIXByT9T3n0fZUQW0tuY+ZmXIIslUI8iPAEpSsl5eT8T4e0KIHgvy4BDyVHzepL62t8kuVle28PPaztjyVH55hb0L5cQ5aDiHrmfh0IyBa7eZxzVnfb3rNAWKbOiKs77my7YL8WMPevWRbUSGv92YVlPzwqJ4lkH6g5KeiIv5j9OlDtps2JX4+ViDIjw4o+Ul35Qdg13aqtCjJDyvnQUl+WBMQnmFvSuVHkB97oRX+lW4EJJ3UF573W5nbw/Oe88p3SgXY4UjSEKIffhDXX8B5UMJusLZ4TBx/PNlu2cJuoh0Q5EcTwaB8E3iSH2XVM1ZQq07K75yGkgSwJiDpGvYmlB/nwJMEuEEB4Znr5Bblp62NhDI5DaXyw+O68ySdqQI7yE9lJVBcTPyXLVvsOS8BAT3Y0We7dSOhns3N8jIjLCDIjwaUs2huKXjA4gVKbQFECaBll3mUfGZNftwS9sZT+RFOi73gWf3LDQqIUH7Y2Ve+s3iTbTGOxAc7HEmPR+T9CLCDHcqPzwf07Ek+b96c+DmZhSA/GlC+SKjyw2oGD4hWXyQpcvVwJ6Fc74ZX+BePsDeeyg9PxUsoP87BLcoPzxCodGy30jYr+2rlxw3ET4wj1mAH+QEE+RFgB7v6LA19Y5n3I8iPBpRry1DlB2BPQHiEnvFMvudp2w3KD+9qb8JpsRduUyHSYb0bt9hWrlTOWvnJyHBHyJ9QfqzBLkeSVwK5QPrBrj7buzfZCvLDGUryo1wzgdVgTl9YRUVyyed0CMFSKj9uyPlJh2sulB/n4Lb8k3RVX3jY9njYVnxThhoqbadDuGGqIJln0QXSE3aEvQFC+XEN6EvM45EdYYDdYK6lgLB4gUqSe5QfntXeqPLDM+yNdcGDggLhtNgNN1Rc46068Sr5rG43y6IDNFeS5XVXEi/WttX2xSRKfHCC/LAK1RdITyQzYRfkRwNU+cnMJCEE9GXG4kUSDMrhdazLLivbx8MR56n80DbyVH54tJs6LVlZwmmxG24o+cxbdeJV8llpW5Ii1XwWtgG291xNvNKpwEaqYP9+sk3UkezRg9yHxkZg9+7Ez0tAQAuSZD/5+fFHdpPOgvxoQDlrCrB9gSttsCY/ShvppvzQ66685qxLXfMIexPrczgHN5R8dovyw1PxYmWfJwFxk/IjyI912OlIZmYCxx1HPm/cmNixBAT0UF8vP+OJ9tnycnlpF1Yl2gX50YBS+QHYDubKFyVrAkJtULWLpe32dvkFylv5YV3wQEvx4rkmi3Ba7IHbSj6nw1o7WtccSP3QM54hd5Ikqr0livp6OeKjY8fEjyeKHgg4DUrWCwpknykRUPWHVblrQX40oFZ+WL5IqMPt9Ua+SFiSn9zcyHwnFraVKgvPgges1TalHR5qm9aMrXBa7IGWCpEOoWduUX7oGAqwLTrghpwflgREuSq7UH7iA3UkCwsj84zjBXUkhfIj4BRosYNEVR8K1hXffGzMJBfcoPzQlxcP5Yfa5GHb4+GTf8Kz4IHyumdkRH7nNETYm3PQyrtJJwKiJtSSJFevdApaBMTvT30CwpN48V5jKBVgV8gbhVB+BJwG7bOJVnqjYF30QCg/GnAD+VETEBYvUDX5Yak6KZ1w1qoTEFnq2g0lvtNhXZRUh1sW+3TDWjuSFLkWjVNIVwKiF3LHMt+I2hfjiHXYTX5EuWsBp5HsfVaQHw24Iewt3ZQfvSITPBY5Ze2oKm3zCnsTOT/2wy0LbrpB+WFl3w0ERG2bB/Fi+SwL5SdxOKX8/PgjqfomIGA37Frjh4J1iXZBfjTgBuWHB/mhCZd0dXKWttUvb9YKiLLgAS9nMTOTb7U3kfNjL9yy4KYbii0AqV/1TE91SvViC2rlR5Af67Cb/JSVkR+AXfUsgfSC3X22Z08S9l9fLxMrJyHIjwZ4Kj9q8sPSGaZOCyV9LAmI+uXNs+ABz7VJeFV7U4artLVFJjELxAee6gtP1UnZp5RFB1K93HS6q06ACHuLF3Y7koAoeiDgLOzus9nZQLdu5DOL0DdBfjTgBuWHZ+gZDwLCe5E+Nyg/rMuLq23Taw6IWVs7wDPvRk/5YRFOwHPyyA0KCM98I56qk8dDZm6F8mMdTpAfUfRAwEkcOEC25eX2HZNluWtBfjRAyY+bcn54zFymC/ECIpUfN8zSC/KT/HCL8qMMPaOhrSxsu6HoAI/cFzcpP6ne7lSBUH4Ekg0HD5ItDa+0AyzLXQvyowFlDgbAN+yN58ylG3J+eCxyynNNFtruQIBNdSzldaeV9gARsmIHeK6hpKU6AanviLuh6hnPRU55EE6eileqYP9+srVzFl0oPwJOwgnyw7LimyA/GnBT2Fu6xazzmi2m9nnk/Civu3KBOx4lxoXjYh94VtJT9ilawISVfb1xJNXHMJ6hu0L5SW4cOkS2TjmSIodTwG7QPltaat8xBfnhDDcVPHDDrCmP2WKWyo+yfTxzftSlgXmEGwryYw94r3qvvK8ZGTIB4qkEpPokipuUn3R5b6QCJEmeRbfTkTzuOPI+bWoCdu+277gCAk1N8pjqBGH/4QfnQ7QF+dEAT+VHnfPDI2zDDZWSWOa+KNunzvlhkSCubLvSUU31EKVUhjJkUan8+P2R1bGctp9uSkC62uY5dvO0nQpobJQdPTsdycxMoEcP8lmEvgnYCUrWfT6gsNC+41ZXAwUFZCz94Qf7jqsFQX404Cblh0fCrhtmD1m2m9rweKITxCkRdgrBoKwSuEFxE46LPVCX/+W12KcbnuV0ybvhHbqrtJ0uqlMqgDqSWVlAfr69xxZFDwScgDLfh+YJ2wGPh13RA0F+NMBT+aE2qE1RMch520rC6fGwXZhRvTo6PQ8WtpX2BfmxF2rlhzX5caOKm27tTpexW6jHiUGZ72OnIwmIogcCzsCJfB8KSn6cLnctyI8G9MgPz8TVVHeEec7Y6hFOFva1yI8blB/huCQGtfJDQxqB9HHE002FcFO73bDGkJhAMQcn8n0ohPIj4AScqPRGwarogSA/GnCTAsIzWdgN7WbxAqVkl+baZGTIxNfptisVAp5J2jzIdipDrfwoFcVUDz3jWe3NjWN3qrdbz3YgwCa/LdnhpCMplB8BJ5B25Gfq1Kk45ZRTUFhYiIqKClx88cXYpDrDCRMmwOPxRPycdtppEfu0trbi9ttvR3l5OfLz8zFu3Djs2rUr8dbYBJ5hb256ebshVp9FgrjattJ+uig/YtbWXijvK1V83KDipnr+XroSEDfm/LCyn+xg4Uju3EkKKwgI2IG0Iz+LFi3CrbfeiqVLl2L+/Pnw+/0YNWoUGlVP1fnnn4+amprwz8cffxzx94kTJ2LOnDmYPXs2Fi9ejIaGBowZMwYBl0wTuWEWzQ3Jwm6I1WdhX20bYHfdtZQfN4S9CfKTGLT6VLqquG6YROFJAlK94prR2O2U/VSaiHVijR+KsjJ54VSncygE0gcscn727wcOH7b/+BSWyM+8efMwYcIE9OvXDwMHDsT06dPx448/YsWKFRH7ZWdno7KyMvxTqrhCdXV1ePnll/H4449jxIgRGDRoEGbOnIk1a9ZgwYIF9rQqQfBUftwwY+sm1YmFfSPywzLsjSoEqZ6cnkqOix7U1xVwxziS6pMobho/ebabJ+lT5rc5ZT+VJmKdzPkBRN6PgP1wUvkpKAA6dyafnVR/Esr5qaurA4AIcgMAX3zxBSoqKtC7d2/86le/wr59+8J/W7FiBdrb2zFq1Kjwd9XV1ejfvz+WLFmSyOnYhnQPe3PDAoEsE8S1HFVWjovSNq3044b+5uQ1TyXHRQ9aoZSsnidJclf59HRRztO12AKPyaNUmoh10pEERN6PgP1wus+yqPjmi72LNiRJwh133IEzzzwT/fv3D38/evRoXH755ejatSu2bduG+++/H8OHD8eKFSuQnZ2N2tpaZGVloaSkJOJ4nTp1Qm1traat1tZWtCpG0fr6+nhP2xTS9eWt1+72duJMZThYHkNNQGiCeHNzeig/PJzkYFBexJWlQjBv3ryI36dPn46KigqsWLECZ599dvh76rhogTou//73vzFixAgAwMyZM9GlSxcsWLAA5513nnMNMAGeyg/vPDKe4V9uqHLntkqdkmR/CWUt2+oxrLmZXfhsrInYDh06YOjQoXj44YdRUVEBIPZErNYY4oQv4rQjySqHQiB9wKLPfv65S5Wf2267DatXr8Ybb7wR8f0VV1yBCy+8EP3798fYsWMxd+5cbN68GR999JHh8SRJgkdnhJ46dSqKi4vDP126dIn3tE3BTcqPG0InAHkFaqdgRAJSmfzwdJJ5V5qjYKUgt7a2or6+PuLHKfAsosH7vvIcw9y00KgbcrwkKbI/OGmbV4in0UTs66+/joULF+Lxxx/HsmXLMHz48DB5iWci1glfxMmcH0CEvQnYD1Z91nXk5/bbb8f777+Pzz//HMccc4zhvlVVVejatSu2bNkCAKisrERbWxsOqzKZ9u3bh06dOmke4+6770ZdXV34Z+fOnfGctmm4IfTMDaoTi8RVCiMSwKroAE/bWk4yy2ILvNb5SXbHRQ9GBQ94LJybLmOYG8ZPN7WbhX0t5Ydl21lOxDrhizid80PD3jZvlsNhBQQSAas8NdeQH0mScNttt+Gdd97BwoUL0b1795j/c/DgQezcuRNVVVUAgMGDByMzMxPz588P71NTU4O1a9diyJAhmsfIzs5GUVFRxI+TcJPywzN0grYf4BP+xXqmnEfOD0/bRk4yK+Un2R0XPfB0CN1SQdANFSvdMHnEs90s7PMk+qwnYp3wRZwOIerendybpibAJfVgBJIYwSA75WfLFueWOrFEfm699VbMnDkTs2bNQmFhIWpra1FbW4vm5mYAQENDAyZNmoSvv/4a27dvxxdffIGxY8eivLwcl1xyCQCguLgYN9xwA+6880589tlnWLlyJa655hoMGDAgHLvPG27K+VE6wjQ/g5Vtlgsz8nyBirA3PlXBUsFx0YMb1ESl/VSvIBjLdrrlG3m98udUVH54TcTajWBQLufrlCOZmQn07Ek+i7wfgURRVycriE712a5dyaLzra1kjSonYIn8PP/886irq8OwYcNQVVUV/nnzzTcBAF6vF2vWrMFFF12E3r1747rrrkPv3r3x9ddfo7CwMHycJ598EhdffDHGjx+PM844A3l5efjggw/gVY6eHOGmUtfKGTx6Xk7BDaE6blF+0qHgAa/ckFRxXIyglwQOsFX01AusCuUnNW0bjZ+pOHmUKhOxR47Ik5pOhRABIu9HwD5Q1Sc/P9I/tRNer/OE3VK1NymG9JCbm4tPPvkk5nFycnIwbdo0TJs2zYp5ZnBj2BtAXmJZWc7ZdhsBEcqPs7ZpuzMy5GpQLBz0W2+9FbNmzcJ7770XdlwA4ozk5uaioaEBkydPxmWXXYaqqips374d99xzj67jUlZWhtLSUkyaNMk1CrJbCLW6fHq6Kj88bbOolqk3fjY28lF+nB67n3/+eQDAsGHDIr6fPn06JkyYEJ6Ife2113DkyBFUVVXhnHPOwZtvvhk1Eevz+TB+/Hg0Nzfj3HPPxYwZM5hNxNKQt4ICZ9/tffoA770nlB+BxOF0vg/F8ccD69eTPutE8da4S12nMtwU9uaWxT55FjxI5Xa7QflhTThTxXExAs+ZeJ7rVgHuVF945920tgK5uc7ZdtvEFYuwNyMky0Ss0/k+FEL5EbALrPusK5SfdIGblJ+MDHIe7e18Z/B4hn+lQ9GBdCJ9qeK4GMENRTR4Vd/SC91N9bybWBXXnCQ/bpi44tXfkhlOJ45TiLV+BOxCqpAfB4X45IWbSpYq7adr+Fcqt5tnqWsjhYDlOj+pCJ4OIc/7qlw41w3r3fC0nZkphx3yICBumDwS44gxWDuSu3YBDQ3O2hJIbbAi7L17k+3mzc4cX5AfDegpP8EgnwXj3KBC8FzvJpVnyt2gEAinxX64IYeNJ/EC+Cg/bloqgHe1TNb3XCg/1sEqf6KsDCgvJ5+dciYF0gMsc34AUu2tsdH+4wvyowE98gOkhyOebrOHbggZcYvqJMiPPeB5X93QpwB3VD2jttva2C0V4LbQs1RWzpMdrJQfQF7sVIS+CSQCVn22rEy2EVrhwlYI8qMBo4pr6aC+pNvsodvC3txQ8EA4LYnBrZMYrJxwgE/FNXWfprZZ2Hdr6FkqVntLFbAKIQJE0QMBe8CSsDuZ9yPIjwbUyo/PJ8dui5eYM3DDGkNuCzVMZbUt1eG2/uwG5YdnwQMgtSdR0nXyKNkhlB+BZAMPwi7IDyOoB3OPJz0qNbnh5e0WEpBOs/Q8VMZUhxucUZ4hUED6LRXgttCzdFigOtnBKn8CEMqPgD1g2WedLHogyI8G1MoP4A5HPJVfYm7LN3LD/RbKT/LCDc8xTyUTkBf15LnQqMcjLx6ZymGkbhi7xThiHSxn0anys3kzKd4kIBAPRNhbCoMO5jzID0/HxQ2zh27LdUqHECURq28/3JD/4RZHWGmbVdEBnpXu3HjdnYRQfuIHS0eye3fi0zQ3kwpaAgLxgBf5sfvdIciPBqjy45bZ+HQKPXOL8uOGxV1TOVQm1eG259gN+XMAqbrGy346TB65ZRwR5MccWDqSPh/Qowf5LPJ+BOJBeztw9Cj5zKLP9uhBIgiOHgVqa+09tiA/GnBD2BvPuHG3LLjpBuUnHQinlrPW3i5CIxKBG5xRt4SvKvNu0mEME8qPmEQxg7Y2ecFRFvkTgCh6IJAYaJimxwN06OC8vexsolgC9vdZQX404IYXidtmLtM5VIeFbbcUPGBZGjiV4YY+5Zbxi+bcOG0/GJRDI9L1uqfb+JnMoI5kRgYbRxIQRQ8EEgNVKjt0iBxrnIRTRQ8E+dGAG5Qf8QJNj7ANt9oGxKxtIuD5LPEMe9Mi8xkZMgFysu3KYgs8wmfdoPa5JeROkJ/YoI5kSYlcHMRpCOVHIBGwDNOkcKrogSA/GnCDQ+qWcqluCHtLhwp7bgn38/nkF7EgP/HDDWReawxxOpxRqz8r7TvZdq0FVlnZBtz73kjliatkBk9HUig/AvFAkJ8Uh9uUHzfMXIqCB87a1mp3IBDp0LGwrVzTSjgu8cNtyg+r9W60+pTSPgvbavvpELrrhskjofxYA8v1UiioI7l7t5y4LiBgFjRUk0efFeSHAdxW6toNCgjPdqfyjK0bHFX1LL0gP4mDp5poRKgBNqFnen2KVdibW1RcnmqfG5YpEORHHyzX+KEoLQU6diSfnVg4UiC1wVP52bbN3mqhgvxowK2lrtP15Z3KpC+Wo8oiTIiHo5rq4Nmfte6rciKHp/LjZH/WU37SQQFxw6SZqPZmDTwcScDZhSMFUhs8+mxVFXDSScCFFwL19fYd1xd7l/SDG8LeRNhG+pb4pnk3waBQfpIVsZxRSSIhhk7AKJyxpSV1CbWS/CgTyHmSznRXr8UEij54kZ8+fYDFi0Xej4B18OizHg+wYoX9xxXKjwbS9UXiVgUkHa65eqacBQHRIz+sHLZUhlEeWTAY6ajbjVhFB1JV+VHmWSmJJU8C4oZ8zVReVyqZwSN/AhDKj0D84BGq6RQE+VEhGJSrIbFWfmKtU5Guq4TzdFpYFR1wk6MqlJ/EYVTwAEjdogNuIF48bAPuWyogHUqrJzN4Kj+AID8C1sGjSIdTEORHBeWMLGvy45ZqRW55gbrBcXDaPs/SwCLszTm4sU+xVBP1CHWqFlsA3JGvyXOJBKH8WAPvnJ/Nm50tey+QeuDVZ52AID8q0HwfgL0jriQ/6brIqdtIn9P23ewsCvITP7SeJa9XvtY8CIgb1Bee7XbStiS5Y6kAniF3IufHGng5kt27k4nd5mZg5062tgWSG4L8pDCU5Ie18sO7VKsbVgnXazcNB3QCeot90rwBHgniPB1VUe0tcfAMwXLDQqNuajeL/qycQU/XiSu9sGEn89uSGbxyfnw+oGdP8lkUPRAwC0kSOT8pDZ7qi55tnkmzblB+gEhS6pRt5TX3eFI/70aEvTkHrWcJ4KuAsKy45ibbrCeu0i1nkmd+W7JCkvjOoou8HwGraGqSn2WR85OCoE52RkZkuVSeOT9uyH3hqfw4bZ+nAuLGggei2lviSNc+5WbVidUaQ25RftIhbDhZ0dgoL9jIg/zQvB+h/AiYBSXrmZlAQQHfc7EDgvyowHM2nL7AMjIiS7XynD10wxpDTttPV2dRKD/OIZbyw6JP8VQTeeYb8cyfU9t3w8QVD+Ll88m/i3EkGtSRzM4G8vLY2xflrgWsQqlUOrVGHUsI8qOC1gKngDxANTU5Z1uUao0OPcvKIp95rneTqg6bID/OwQ2J/24i8ywnj9yk/LDOmeRZ7Y3XeysZocz34eFI0rA3ofwImEUq5fsAgvxEgQ7kPMkPj1lTpX2t2UO/39n1btyY+O+GUJ1UdVRTHW5QIdxE5lPdthuVH2Vfc7JgDE+VM1nBu2oWVX727AGOHuVzDgLJhVRa4wcQ5CcKVPlROw75+WTb2Oicbd4z8UZx40D6KiDpWvBAOC3xw40EhKf64obS7SyuucfDPlcUiF10gHXBGEBMohiBN/kpKQEqKsjnzZv5nINAcoF3n7UbgvyowDPsjafDJElyuVY3LcyY6mFCbrDNw0lOdbiRgLhBfUnVdX545lkBxsqP0/aF8mMdvMpcKyGKHghYgSA/KQ49Z5Sn8sMzbMPnk2cyU708b7qFnvEM90t1uJmApGrInRuUHzfla9J8Saft8257MsINjqQody1gBSLnJ8XhhoIHRgTAqdhtvYRdVuvduFn5SfXqWCJcxX6kO6FOt0pzPMcvQLu/ZWTI77FUrS6YrHAD+RHKj4AVpHXOz9SpU3HKKaegsLAQFRUVuPjii7FJNW0gSRImT56M6upq5ObmYtiwYVi3bl3EPq2trbj99ttRXl6O/Px8jBs3Drt27Uq8NTYgVsEDJ5WfWC9QQF4bwG7okR+l/VTN+XFDWWI3FVsQTkviSNdy025QvNw0icFzqQCl/VQLG052X8RN5EcoPwJm4IY+aycskZ9Fixbh1ltvxdKlSzF//nz4/X6MGjUKjQpG8Oijj+KJJ57As88+i2XLlqGyshIjR47EUUVJkYkTJ2LOnDmYPXs2Fi9ejIaGBowZMwYBJ8uJmUSsggc8S10Dzr1E9cLeAHe8QFM18d+N4X5O3+9kd1zMwI3KT6ormbEIJ0/iFQhETjDZCZqraWQ/1SaPkt0XcUPODw1727w5sg8JCGghrcnPvHnzMGHCBPTr1w8DBw7E9OnT8eOPP2LFihUAiMPy1FNP4d5778Wll16K/v3749VXX0VTUxNmzZoFAKirq8PLL7+Mxx9/HCNGjMCgQYMwc+ZMrFmzBgsWLLC/hRahR36o8tPW5txLLJbjADjnuPBWftzsuKTbTLnT1zzZHRcziOUQpqoKwTOU0g3PsdHY7ZR95didLuGzye6LuMGR7NaNRLi0tAA//sjvPASSAyLnR4G6ujoAQGlo+mLbtm2ora3FqFGjwvtkZ2dj6NChWLJkCQBgxYoVaG9vj9inuroa/fv3D++jRmtrK+rr6yN+nAIlP8pkUUBWfgDn1B+9l4hysU8Wyk+Gqle4Ie8mVcmPG8JVWNtOdsfFDHiqiW4oOuDGkDue6jHAZuzmubgsz2pvyeaLuIH8+HxAr17k8/r1/M5DIDmQ1jk/SkiShDvuuANnnnkm+vfvDwCora0FAHTq1Cli306dOoX/Vltbi6ysLJSUlOjuo8bUqVNRXFwc/unSpUu8px0TegUPsrPllZidIj96L29qH3B+9tDrjV5x2g0OW7qF3LmB9LHK+Uk2x8UMeDqEbiDU6RZCqne/ldUy0035YUV+ktEXcQP5AYDQ5YIqolhAIALBIHD4MPnMu8/ahbjJz2233YbVq1fjjTfeiPqbR+U9S5IU9Z0aRvvcfffdqKurC//s3Lkz3tOOCT3y4/E4X+5a7yUCOO+46L28AXc4bKn68uZZdMANBQ+S0XExA57hX25QftxUwIPnJAbAbuzWsu+GkGWnx5Fk80WUjiTvWXRKftau5XseAu7GkSNyXlhak5/bb78d77//Pj7//HMcc8wx4e8rKysBIMr52LdvX9iRqaysRFtbGw7Tp19jHzWys7NRVFQU8eMU9MgP4Hy5az0CALBTfoxUJ6deYnoLrLKwDbhztjpVY/XVSDbHxSzcqPykq23an/1+5xK7eU4eGSk/qZ5rlYy+yJEj8pIVvMnPgAFku2YN3/MQcDdovk9BQXRKSLLCEvmRJAm33XYb3nnnHSxcuBDdu3eP+Hv37t1RWVmJ+fPnh79ra2vDokWLMGTIEADA4MGDkZmZGbFPTU0N1q5dG96HJ2gpaa0b7HS5a7eEvanhNAkwU2ku3ZQfN5A+p8lPMjouZuHGcMZUVxPNLBXAc/KIZ75mquV5JbMvQkPeCgv5O5JU+Vm/PrIfCQgokWr5PoBF8nPrrbdi5syZmDVrFgoLC1FbW4va2lo0NzcDIDO1EydOxJQpUzBnzhysXbsWEyZMQF5eHq6++moAQHFxMW644Qbceeed+Oyzz7By5Upcc801GDBgAEaMGGF/Cy3CSPlxuty1G0IneL+83UQC3DBbnYq2k9lxMQueeTduqCDoJuWHRbVMI+XH6XtulK+ZqmQ7mX0RN1XN6t4dyM0l/WPrVt5nI+BWuCVHzU5ouLr6eP755wEAw4YNi/h++vTpmDBhAgDgrrvuQnNzM2655RYcPnwYp556Kj799FMUFhaG93/yySfh8/kwfvx4NDc349xzz8WMGTPg1XpzMIYIe9O3zaLMNmtHPBh0Z8hdqjotAHFcZs2ahffeey/suADEGcnNzY1wXHr16oVevXphypQpuo5LWVkZSktLMWnSJNdMoriZgKQq8dKznZlJSIEkpbbyw2PSTGmfJeFNZl/ETbPoXi/Qrx+wfDnJ+6ELnwoIKJH25EeigaoG8Hg8mDx5MiZPnqy7T05ODqZNm4Zp06ZZMc8EZpQfHgUPWL1AeYS98VxjyEyZ2HQreOC0k5zMjotZuJGAuEFN5FG63eMh172lhc/4yTNkOVWrvSWzL+I2R7J/f0J+1qwBLruM99kIuBFuUivtgiXykw4wk/PDo9Q1q9AJt4W9sWq3lm03zNLzVH7oqvRafSIRJLPjYhZuJCBuyGGjRQfUuSlO2qb2W1r4jJ9uCFnmofywLJySTHAb+aFFD0TFNwE9uK3P2gEHXkHJDaH8RP+NJQFhnbDLm/y4ueCB0/ZTGW4kIDzVF5ZFB3gWbXFbyLLz+Xt8Qx2TEW6bRRflrgViwU2hmnZBkB8VRM4Pe9tKx0EvYZen8sPDWXSDQgAI8hMv3ExAeBIvIDWLtvDMmeRJvIwqdQryow23OZJU+dmyBQjVixAQiIBQftIAPJUfN4S9Gb1AedjmWWY7VePlY9n2euX+L8hPfHBz+XQehFo5nvJUfngQL1Y5kzyJl5Z9EfamDbc5kpWVhIgFg8DGjbzPRsCNcFuftQOC/KjAM+fHDeoLj5c3z5h1es09nuiQu9xcsnVyNswNCgGPe57qcOMipzyLaHg8qZ23yFO1d0uxGqH8mIPbHEmPRyx2KmAM2mfLy/meh50Q5EcFM2FvTuf8pFvYG0/lx6jdTit9SvtGjoOJ+gBxgXeJ3FSGGxcapX2KFrJwAm4lAawUEJ4TOG4rViPIjzbcRn4AkfcjYIwDB8jWTX02UQjyo0K6L3LKM+zNbbH6LMiPmRwJqkbaDZ7hMqkONyo/LIsOuO1ZZjWJ4tZiCzzXaBNjSCTcOIsuyI+AEdzYZxOFID8q8Cx44NaEXZ5hbzxDZdyg/ACpmSOR6oiV88NznR8gNZ9lnuNnuo/dgAh7Mws3zqKLsDcBPTQ3yz6vm/psohDkRwWjnJ90LXWdyvHyRte8oIBsWZAftX1l/0vFtUlSHW6s4ufzyXltqfwsu7VoC89iNU5fc62cSUF+oqF0JN00i96vH9nu2gUcOcL1VARcBqr6+HxAURHfc7ETgvyo4NZS125IFuZZKYmn8uP3Oxd6pkc6MzJkAsRzxliQn/hgpuKaU7lcbgifdZv64oaKa6lYMEbkDVqD0pEsLOR7Lkp06AB06UI+i9A3ASWUSqV6KZJkhiA/Kri11HU6FB1wm/JD7zcANDSwt5/KzmKqw0w4I2tCrbSfiqGUbi+2IMZuAepIlpe7z5EUeT8CWkjFfB9AkJ8oUIeEp/LjNkfYDbZ5KD+ZmXI/SOW1ncSsrb0IBmVVh0fejRhHov+Wys+SW6+5ID/RcLMjSfN+Vq/mex4C7oIbc9TsgCA/KlDlx2idHx6lrumaM6lYac4NM5da7QacVfskyR0zxqLam70wSgJXjiupTAKE8sPOtluvuZhAiYabHcmBA8n2u+/4noeAu+Bmwp4IBPlRwa2lrlmF3Gm9xFJZ8eJJfoJB+TNP1Us4LvbCaO0TZS5XKpOAdFMhhNrG3nYyQhn25jb85Cdk+913ke8mgfSGmwl7IhDkRwW3lrp2uvKYmapnTuW9mJmp9vsjnUq7wJP8GK2RAfBVvQT5iR+x7qsbHNJUJF48ybzR5FEqK15mbAeDzi2qm2xw8yx6796krzY2At9/z/tsBNwCN/fZRCDIjwpGOT9KR9iJSk1uICBaLzHabqeT/o1eoIAzL/BY5MdJ0ql0CESlptSBkfIDuIMEpGLInVB+ov/G85or89vEOELg5ll0nw848UTyedUqrqci4CK4uc8mAkF+VDCT8yNJzjriPAlILNXJCdJnxmkBnHmBGtkGnFV+YjnJNM+rudl+28GgHNogqr3Zi1ikNh1IgFtVCB7KT7qqbU6P3ckIN4e9AXLo28qVXE9DwEUQyk+awEzYG+CsM8wz7E3rJUZtS5IzjriRbZ9PJqJOhBuaDXtzgnTGCo9y8p7HIl5C+YkfymurXvgRSO0kdLerEDxDSNNN+fF65e/FOELgdkeSkh+h/AhQ0D4rlJ8UhxH54emI88y7UZI+J+zHUl+cbDvPnB+jqmAAm3br2RbV3uKHknxoreXBM3fQSTURMFexMhVti2uu/Xen7Scb3B5CJMiPgBpuVyvjhSA/Khjl/ABsEuCNHOGWFmcT/7VsZ2Q4W+Y71guUBQnQs83ifmdkaDvJrMiPUH7sRaw+xap6otsqN7qBgKTiNTczceX3yxN7rGwDgvyo4XZH8sQTybuopgbYu5f32Qi4AUL5SRMY5fwAzr7I/C3kDWpU6hpgH3IH8FVfUlX54dluEfbmHHg6hMq1o4ycYacdcZ7Ey8h2uik/StXeyWgFofyYg9sdyfx8UvUNEOv9CBAx4OhR8tmthD1eCPKjglHYG+CgAvLOOwi89Q4A/URl+oJxhATs2K1rG3DYET94BABf5YdHtbfAytUh29pVJBxt91+mhD+LdX7shX8TqRPLQ/kJbtwc/sw8DGrdOvgbyIGZk4ANG+DftlPXtqPE64cf4P9yCQAOpG/vXgReex2AdruzsmRV2fbr3tCAwF+n6NoGBPlRorlZfo+42ZEURQ/SEEePAnfcAfTtC4waBSwh4xkl6xkZQHExx/NzAIL8qBCL/Di20OlXX8EP8ubUepF4PA46w3V1CLw8A0Ds8C9HHPGH/gYA8EE7LsOxdi9bBv8f7ia2eSg/Dz8CAPBK2otgONbu/fvhf/IZAEBGhmSYlC+qvVnEjz8iMO4SAPqk1jGHsKkJ/tPODP/KnAT89KcI7DvIx/aIEQhs+V7XtqNO+NVXI/DdWl3bjrb7T3+Cf9m3ALTHbo/HQfuvvAL/osXEtkd7VUx63cUkiuxI+nxAURHfczGCyPtJMxw5AgwZAjz5JLBhAzB/PjBsGDB/fkSOmpafkMxIseYkjlg5P469SA4dgh/kzcmcgPz4I/w0bCNDO6HIMQWkpQWBfeQJ8/m135COkYD33oO/Zh+xzaHaW4DaztB2HBxr94EDMfuaUH7ixKJF8LeT++kNapN5x8aQ775DoF7uLEbKj+22d+8m5MugXznW7vZ2YM8ePrYB4JtvDG3Ta97e7sBin6tWIUAnzSTt/uYY8du6Vbbdpn1hhfIjQxnyppXj6RYMGkS2gvykASQJ+PnPgbVrgcpK4LXXgHHjyGD1i1/g4HYS8+bWMM1EIMiPCrFyfhxTAg4dkl8krEOw9u+XX96sVYh9+xS2jYmX7bYPHw7b5qL8HKoHoD9r6li7FUTb59VWJ0S1tzixZo38HEP7WXLMEV+5MnxfAWMVwnZnNBQjYzSGOeYIb9kS07Zj1zz0gIRtazxPyrwb29t+8KA8frZpH9yxtm/cKNsOtGnuIsiPDLcXO6Cgys+mTc4trSHgErz+OvDxx+SFP3cucO21wBtvkMSv2loc+PdcAO7vs/FAkB8VzOb8OKn8MM+72bcv5uwhE9s6ziITEsCa/LS2wt9IHCefC8mPUH7ixK5dMScSHHMId+4MP0sAYxKwZw8A8FFfTNh27JrX1kbabo9+YOizBNjcdkkCamvl8bOdMfmpqZFtBwX5iYVkIT+dOgHV1aR7CfUnhdHcDPzpT+Tzn/8ss968POCvfwUAHPzwawBC+Ul5SJL5nB8nlB9uYW9K9SWgTX4ca3eE8sOY/PBUfvbvlx2HGKGGTpAfo5lqQJCfuHHgAD/lp7Y2QvlhSgJqagBwUl9CBMSM+uK47Zboh9Xjcei6Hz4MtLcrlB/GoWd798q2/drJgYL8hOD34+BSolAmgyN58slku3w53/MQcBAvvUTClbt0IcUOlLjsMuC443CgmQycbifs8UCQHwUCAUKAAHcrP4464qyVn717+dnmqfwoQw3BMdxPJ99IkJ84ocyn0sn5cVKFoM9SBgJsF1hVKyA81Zfm6AeGttv2vBu17aajmrs5ct1Vtn2t2gd3xLbfH0n0A9oDhSA/IbzyCg489W8AyeFICvKT4ggEgKefJp/vuSdSngbIAH7ddTgIwtSTgbBbhSA/CigXgWOa8yNJlnJ+nFR+9MIX2OT8MCY/JpQfJoSTY7ifXr6RqPYWJ5QOoc6zxEL50SPUzBQQT7R9ZrYbjkTtQp1wwGZHXE1AmrXJjyMkQN1ulsrP/v2AJAnlxywuvBAHQFhPWbYD1XNshiA/KY6PPgJ++AEoKQF+8Qvtfa65Jtxny/McWpyNIwT5UUBJfpgqP01NQFtbzLA3J0sf0xeoXuIqi5A7n85MeUoqP0rSx1r5UbY7BvkRyo9FKJUfnTBOx0iACULtmDMaSmgIt11DhXCs2ILadkv0w+pY3k2ohFd4/GSZd3P4MADEJCCO2Fa1W2/sPv54YOhQElmT1ujcGQfLyOqh5TVrOJ9MbAweTLabNgH19XzPRcABUNXnV7+KrMiixHHH4WBBNwBA2e7VbM6LIQT5UYAb+VG9xHg64rGUHydscwl7CwaBI0cMZ6oBB0mfS5QfvXaLam9xoKkJaG6OSeYdIyB1dfyUn0OHIAEI0j7dGj1QKMtsS9qpZvEhNH4aJf47tt7NoUMAFGM3S/Jz5AgARbsDDMlPXR0ABfHSmTT7/e+BL74Arr/eRttJigMlIfKzdSnnM4mNigrg2GPJc/rtt7zPRsBWbNgALFxIZtlvvdVw1wOF3QEA5RsXszgzphDkRwG6xo/HE7vogK0kIPQC5Zb7osw/0XmBOpnzI+dIMAy5q68HgsGYpE95v2112CLKixsrP+3tct+0BYcPK0ifNvESyk8cCCkQ8nPMMOzN7wcaGiIJdSC6Xzm2zo8ibBfQVl9ouwOByIkmO2wDxqoT4BDpVI/dGtXeHLOtJiAsbYeIlzx+ivjYWDjg6wQAKNu0xIEFn+zHKaeQrQh9SzH8m+SeYfRownANcDAjFKq55gubHSD+sEx+/vvf/2Ls2LGorq6Gx+PBu+++G/H3CRMmwOPxRPycdtppEfu0trbi9ttvR3l5OfLz8zFu3Djs2rUroYbYgVhr/AB8Zw+ZlJvWmcFLOdtqtU2H9FHyEwjYTECUahuMK+wBNrddqfzoKARFRcAVVwBXXmmj3RBSdgxRh1/FUH5sHUNCsSkRyo+GAUdCzyQpglAD2lXPHFvvRk1ANFQnpX1HQ89YrrWjVn50Fol2UvkJh/v57RwczSHZxpGDtHJW849JIaeIvJ8URDAIzJxJPuvl+ihwoIHMgpYf2QJs3OjkmTGHZfLT2NiIgQMH4tlnn9Xd5/zzz0dNTU345+OPP474+8SJEzFnzhzMnj0bixcvRkNDA8aMGYOAxkwlS8Qqcw04q/zEeoE6EoLV1gYcORIzbpxJ7ksM8nNUO5c4PqgJZwzyA9jcdmXYm46TnJkph585R360ZyBLSoDZs4Hp0220G0LKjiFU+cktBKBP5h0hINQRziYd1ge/Zod1xBFubibrVinLbGsoP1lZ8sr2jkweZZBZq1jKjxO2A9nkwuoVHXA09CybDJCxlB9HbBeUANCv9uYkkm0cOXCAdP5yHAA+/9z249sNQX5SEIsWATt3AsXFwNixhrv6/UBdHemzZThI4ldTCDrZJfoYPXo0Ro8ebbhPdnY2KisrNf9WV1eHl19+Gf/+978xYsQIAMDMmTPRpUsXLFiwAOedd57VU7IHH36I9o0AMMaQ/DBRfnReoI7k3ahDdXRmDx3Lu1EqPzqhEyyUHz3Sl5lJnLa2NnLdS0ttsr9/P/zoSGzrhDkCpO2trfa33Y/uIdvswy9Sdgyhyk9FFbAjdF/9/qgkPkcd4cISoDWk/DREsytlCJQkQbMctmXQZ8mbAyokapWbpnk3jY02Er9QpUwgRH6C2sQLcIh0UttZeeS6s6y4RglvfhHQGjvfyBHbxaVAg/746SSSaRxpaZHf22U4CPzvf7Yd2ynQogfff0+6uW3vPgF+eO01sh0/Prq8tQqhoQ0ej4QS6TCweDFw880OnyA7OJLz88UXX6CiogK9e/fGr371K+zbty/8txUrVqC9vR2jRo0Kf1ddXY3+/ftjyZIlmsdrbW1FfX19xI+tCAaBK65A2x/uAcBP+YlVstQREhC6N7FIgGMEJBBQ2GYY9qYmnH59z8CRe25C+QEcaHswGFniWyfkjjfsHkOYgFbAKiex/Xrqi6M5GAUdZNsanUYZemZbPhe1XSwvBpHZYlzy2Tbi19oajkf1e8jAndnGMOyNks5M0rBMliF31HZIadRTfhy1XUQ8Yh8H8mMGbvFFQkMDvBkSilEHLFsWf6MYoaQE6NGDfF6xgu+5CNiA9naAhoZec03M3UNzeSgpaIcXwZTrBLaTn9GjR+P111/HwoUL8fjjj2PZsmUYPnw4WkMLhtTW1iIrKwslJSUR/9epUyfUhtYtUGPq1KkoLi4O/3Sxu27m0aNAUxPaQV6evHN+9MI2HAl7278fQGzlxxHbKuKlF3pGCUBTk2YOd3xQ5/zoOA6Ac22PlRsCOEB+QoUeYhXX4AknxhCAwSSKUn1BSH1hFXpGbecXAwAy0W5IvGy1Hw6B6gAA8CCoqfwADrQ9ZBseT/h5ymzVtu1o0QEfmUWNtdaOM+SHDBJ6pM/JdgcKOwAITZoFtcvm84KbfBHqSJaVSfBkZJDQI4Oxyi2goW9JwNUEYuHLL8lEVceOwBlnxNw95BqiojJEE1Ks7rnt5OeKK67AhRdeiP79+2Ps2LGYO3cuNm/ejI8++sjw/yRJgkcnBuPuu+9GXV1d+Gfnzp32nnRoIKfkJ9Or72HTl7eTOT8+nZeYI2FvagKio4A4aTvWKuHUNmCj82CyyATggPLT2grU18esNAc4QH7U7TYgXrzgxBgCMJhEoUUHckLOKNo1b5wjjnBIfWnP7wAgpPxoJMn5fLKybZszTMfPwlLZts7DYnsIFn0ZFxaiPRgaR3QWGrWdeAUC4WvcnkGS87QKPShtO6H2tYdyfpjmG6n6WybaXFca0k2+CFV+yjtmACecQH5JAkZBK74lQZSeQCy89x7ZjhmjX85YASqSVlT5gK5dyS8ppP44Xuq6qqoKXbt2xZYtWwAAlZWVaGtrw+HQzDvFvn370KlTJ81jZGdno6ioKOLHVtCBnJKfxiO6u1JH2JGkWUoCdGLWHQn/CtF7udiCMQFpbLRxgm/v3kjb7drKT04OkBHqqba1Xa386DgOgAPkR51nZVAm1vZ7rm63AfFyC+wYQwAGkyghR7w9K+SMwq/pEDpS8lml/OjZVtq3bQyjpC+k/GSiXffgthM/qvwUFcEvkecps42R6qR4KGmxhcx2DgUPMsnBM3UmrhxVnZREv9WdoW8UPH0Rn4+oKAMGAPjpT8mXScAoaHG8r79OuUrH6QVJksnPRReZ+pcw+alAStY9d5z8HDx4EDt37kRVVRUAYPDgwcjMzMT8+fPD+9TU1GDt2rUYMmSI06ejjdBA3obQC6zugG58FZOCBzpx407m/BgtEKi0DdjYdmo7FKuvF3Ln8QCFJKzdOQWEpfJDCacnVJ0q0Kb7ZnFa+dFbW8lNsGsMcXwShZKArJAzinZD8gPY+CzRCZzcIkPbgHMEJEJ10nGEnQp7k4qKEQiS1xmztXYo8crKQrtElXP2a+20+3INbTuiOtF7rqwu6HLyw9MXOftsIvTMng3ZkUwC5WfwYKIU79sHbN/O+2wE4saaNcCOHWQmeeRIU/8SQX5SsPSf5WpvDQ0N2Lp1a/j3bdu2YdWqVSgtLUVpaSkmT56Myy67DFVVVdi+fTvuuecelJeX45JLLgEAFBcX44YbbsCdd96JsrIylJaWYtKkSRgwYEC44gpz0IG8ay9gB5DV3gh8+KEmQ6aOcGsr4Ucm1MPYoA6pJxOQAK9O6IQjifc07C07n1QrMnAcPB7iozc0RJKhhG3nF4cqBumHTRQUkNtkuwKSUwC06BNOwIHrTklfSTlwSDFLr0zICMEx8hOqzKVXjtlJpOQYAsjjSIj86KkvtOSzJBGHtLjYPtv+vEJD24ADzrAq58eIeDkV9kYT7wGGBQ9oyF1xMfwh4sXMtiQp8o1IyF2mn33Ym58SL4P+5hSSdhxRKj+2lVx0Bjk5wKBB5FS//hro3p33GQnEBar6jBwZOftmgFQnP5aVn+XLl2PQoEEYNGgQAOCOO+7AoEGD8Oc//xlerxdr1qzBRRddhN69e+O6665D79698fXXX6OQTt0DePLJJ3HxxRdj/PjxOOOMM5CXl4cPPvgAXluYRBygM2gdSOnhTLQDL72kuasjs7bUEacKiE6yMHWEW1ttDJehBQ+ow6YT/uXxOEgCCshstZH64hgJyAvZNgh7sz3fiSo/pRUA9BekVNq2u92BUtLX9YpMOImUHEMAbeVHYzaclnwG7Fd+/DmFhrYB50gAVZ2MVADHVKdCmfzEKvnsRMhde8BYOXck5C4UfxzON2pv0VSQnS30ECJeBoTXKSTtODJgAJkBOXwY2LbNOTs2gYa+LV3K9zwEEoDFkDdARX5OOon88sMPcg3sJIdl5WfYsGGQDII/P/nkk5jHyMnJwbRp0zBt2jSr5p0BfYEqQ0Z27NDcNSdHnrVtbJTDsRJCVM6PMfkBiO0OHWywTdWXkMOmt8Aqtd/QYKMjTnN+QtddL9+I2gYcUH6ow6ZzzQEHw96KFAniTU1AWVnUro61u7QC2M9nccKUHEMAmQT4jJUfgDikjY0OkICc2MqPU+FfVvKNbLddKFfs0lNxHQt7Ky6G/zCZvdcjP47Z9vnkEt9oI7NiqnKlTuYbtWeEqtxxCHtL2nEkKwvo2xdYtQpYuxY47jh2tuPA6acDzzxDlB+BJMSuXaRQgcdDih2YRAT5oXXPv/8e+PZbgGeEhU1wPOcnKRCaNW3LUZAfnYHc9lnbtrawZxvOw2jSrlaUlSWvl2ibM0yVn0zjUq2AAyWfKfEKrVPBRfkJlYk1Q35sv+b5CkeVsfLjL1EoPy4rUZu0oMpPqOwx0/CvsPITOwHdKQWEhtyZabftxRby5dhBZkUHaNhbURH7sDdKfjp0CNvWIyC2q21+eQ0pv5ef8pPU6N+fbNeu5XseJkCVn1WrbFYPBdjggw/I9rTTAIOCQGpQ8tOxY+iLAQPIdt06+86NIwT5AaJmTbPQZjiLZasSQCvNeDzwxyjVCjgQgkUJSHidCv0DO2U7QAkIS/ITuu6BnFDCrs7aIIBz4X7+POKw8Qh7owtSGtkWsAg6jnhjz4Y75QxHJKAzJiDtORzD3gpk5SejhXGlueJitPtDyg+rdX5CZBfFxWgPKMiPQYEN28uLA2gPFW1JhoIHrkISkZ+uXYHKSsJ5U6jScfogjpA3QKX8AEC/fmQryE8KIew4mCvbaavzEHJGgx0UMetN+gtJ2eoMt7SE16mgM3jeVoYEhIa9hRw2b3uzrgphq22l2pYVchZ18qwA58LeAsrkdJ2DO0Z+QmFCPvhtLh+YppAkS8qP7WFQVPkJ9WemttWLbbJUvKIWd22Dp82YcNoeelZUBH+AkJ9Yyo/ttjt0gD9EvPTeW8r3lS3liqntvDxZ8RLKjzUkEfnxeEjoGyBC35IO9fXAwoXkswXy09Ymz6+EyQ/ts4L8pBBUi8XxID80DAkwJj+2hmDRJXx9PgQyQsUWDKqe2eqINzeHiVcgO3aOhK22Fes6+DND1YoM1DbHcn5CIUpMlR9llTsI8mMbmpvD5fFpEjhT9SVcaS526WHnVCdFn2Kl/FDVKY/f+kYRyo/O+OlUgQsUF4eL3+i1XVlE0hZ+YsG2gA6oI7lxo43Vi5yDKHqQpPjkE9K/evcG+vQx/W8K11DOLVcqPymw6JMgP4C8zk+mYtaUVdgbJT8dysNf6eX8ADaHntEeXlEBv0S6gldngVXAIeKVlSWrTqxIACU/HTrA7wmtz9F8NOZaO7YrP7kKZ5F1tbcchZMsyE/ioI6wx4N2ul6YwThiKwmQJFn5CZF5puv8UMUrh7/ywyPkDsXF8PvJR1+wFeFfWNju0CFsTq+/KcmPLdfdgm0BHRx7LBnc29uB0KKrboZS+UkBvzd9QEPexo2z9G/KfB+6wDx69yZru9TVAXv22HeOnCDIDyDPXIZWyg7n/Og85Y4rP411urvb6gwrenh4gcDWBjYkQBFQGgiGZk1ZkQBaqrG0VC4yIbXrHtyxnB8eyg/tb8q1aAT5SRyU/BQWhkOgmFU9a24OO9x0DDOj/NhNQNqzYis/jqlOJhZ3dSr3JVhQBEkyF3pmd5ijGfUlM5P8ADZdd2Wuk1B+4oPHI8+kJ0Ho2+DBRAWoqRGLnSYN2tuBjz4inxPN9wGA7GygVy/yOQVC3wT5AeSwN+X6HMGg5gwe4IzyE+gglzk2Ij+2qi+KHu4PERAvAmxCz0L5PqioCF9m5spPSUlkpaR67XBDW+93W5s8W60M92Md9pZFpoO9CAjyYwcsOoS2kgDqCGdkwJ+hUJ1YV1yLsbgr4LzqxCXUML9D+Cs94udY3o1CdTKjNNpCvhTEK8K2ID/WkER5P3l5hAABwJdf8j0XAZNYvJg8q+XlsnRnEprkB0ipogeC/ADyS8ynCBkB2MxeqqpvAYC3IbbyY2vYW8eOptQXR4hXp05yyAgP5cevaHed9nW3td0HDpCt14sAzTdi1e7m5rD3488Uyo+tUJY9NuGM0jHEludYK/eEFQFpbQ23kRYu4bHAakSZbcb5Rv6CDuGv9EgAtS1JNkWHKe55xPgZg/jZ3t+URF+EvVlDEpEfADj7bLIV5CdJQEPexowh4WoWoEt+kqzPGkGQH0DO+ckwR34cyfkJLXiZgQA8jfrOqCNhbxUVkSQghiNue9gbyRMnKoTO1KRjyo/ScWCh/FDCWVYWDo/yIsCm2httt9cbVggE+bEJCvJjRvmx9VnSIF5GzqitfUpZ9tinIPM67bY9hJQWPMiOvbir7bl7qmILYfsa15222zb7Gv3NiPg53d+E8hMH6Cz6+vV8z8MkKPn573/5noeACUhS3CWuAaH8pAfa28POPl2fI4ul8kPXmykm5McHf0TlKDVsdVwUyg/z0LMI4kU+miFeR/VrQZhHhPKjsB1D+XGK9Jlpt0GXMA/a7pKSSKVPkJ/EYdEhtLU/a4TcmbFtK/nJzzdV9riw0EbbwaBcql8Z9ub3az4stoeQaiywqke+fD4gJ8dG+3qEl+U9F8pPYjj+eLL9/nvdEHs34YwzSKrS5s1AbS3vsxEwxNq1JDkrJwcYOdLyv+uSn759yXbDhqSvfCHIj8Lhbc8IrVbtC91UFiErWuuuGBzcqZwfK4643cSLuW095YcF+YmTcNpin7ZbTfoE+Ukcejk/OmOIrSTAovLjlG0z7bb1OW6Qi7O0K9c3AjTtU9ttbeQnYdDcvTxSbCEDAWRAP67N1rZb7G+OkG2h/CSGY44h8ZB+f1JUESgpAQYMIJ8XL+Z7LgIxQFWfkSMjZWeT0CU/PXsSBlxfL++UpBDkhyZvFhSEV8rO9IUW2owR9mZrzk+I/HgRYgI6b0jHSl2bcMRtJV4a6gsz1UlP+dEJe1Ne84QnOzTIjxHpy86Ww3UTbrteuwX5SRxxKj9OkR9mtvUS71m22+eLDOMEDMkPYG/4V3uOIuQOYKP2uay/iWpvcSAjQ66etXkz33MxCRH6liSIs8Q1hS75yc4GunUjn5Okz+pBkB/Fy5vOBmZ6Qx4ui6pBauXHY4782Kq+lJdbUl9scRwU6kuyKD+BgA0zxgr1xUy7PR4b264Ie4sgnIL8JI44c34cVV942o6hOtlKABSFHsLKj07J5+xsm+y3tYVt+HNDxRY8+sQLYKS4sQg3tJhvJGCA3r3JdtMmvudhEmedRbaC/LgYu3cDy5cT52Hs2LgOQcMaO3XS+CPts0mwPpURBPnRCB/IyjSn/Nha6jqfhE7EIj+2qi8aYVDMy00ngfKjVI0Ttk+VRsUCgUbtVtoXyo+LYbHaGxP1hUUIVJwKhC2hZxrhVz6P8dht23VXXDwache2zVJx49HfhPJjH6gjmSSz6JT8rF4tv8oEXIYPPiDb007TYS/GCAZl8lNVpbFDkvVZPQjyo3BGw7NYvtDfnC54EAjIK7OHyqV66Qs0RvWvhIlXICC/QN2kvpiwnXDomZ7qpKP8+HxAFomqSfy6K/pbhG2DA9t23fVUJ0F+EofFdX6cIiDcVAB1uwMBzSRuW6ueaTnhGYwICL3feXnwezJDtkMPldMERJLkBVYLi8PjITO1T6/AhlB+rCPJHMmqKpL2IUnAV1/xPhsBTSQY8nbgABm6PZ4Yyk+S9Fk9CPKjNZCHVsN2XPmpqwt78jRpNvzydjrsTWE7WFwSfoGayflJuN2SFFl5zEKpa0myYaE+i9XeABvbHofyY3vYm1B+7IfLcjDMqgAJTyRoqS8GeTdZWfJEQsJt1wq/YkVAtK45te30PW9tBW1we25R+Gueap9QfuIErfiWJGFvgJz3s2gR3/MQ0MDRo8DCheRzHCWuAaCmhmzLyxW+sBJJlqemB0F+tHJ+YpAf25Qf6owWFCCQQWcPQ96I02FvVAXIzw8nCwOMlJ/mZjnmxaTyQ695wvYlyfI6P4Cz5Meo3YCzOT+C/NiEOHNfHK38FcMJ9/ttCD3TI16A83k/Wgt9es0pP7bZVt5vkyF3ttmGXOIb4JvnJaq9xQk6i757d9KMw8OGkS31sQVchE8+IYN6r15Anz5xHYKSH82QN0Dus1u32rD+Bj8I8qMR9kZnJh1XfrRm4r3mlB/byh4rlBeA0Zoz1LbPR4ifiZC7jAybiF9jY3jWlLfyY6bdgLPKjyh4YBPcqPzEWGgUcIoEMFJAFKqTrPwYExDbSKfWNfcat9s227TdhYXhCqWAsfJjm21FyJ3Z0uoCBigtBcrKyOetW/mei0mcey7Zfvut/EoRcAmUC5t6PHEdgpKf6mqdHY49ljjJra3Azp1x2XADBPmJI+zNduVH6YyGyho7HvamoQIA5sLegARJgIJ4weNhG/5FbWdmknh9k+THNtKZQNibWOfHxYhz0cnGRpJgapftKNVJI67N1gU3NdSXWNUynQw987GyraW2xSBetrdbcc0BRspPc7Ocy2VyUV2BGKChb0kSRlRdDZxwAhlaPv+c99kIhNHeDnz0EfkcZ74PAOzZQ7a6yo/XSxK/gKTps1oQ5Ie+xJQFD7JCjNnpUteayk/ob06TnziUn5wcosAACTriCuIFgK0CorjmSuLFI+yNm/KjLvQgyE/isJgEbmsOm1HomU5cm+0qhJJ4xVgk2gkSwM12hPLDqNiChm2PR2KzwCq17fGQkGmh/CSOJCt3DQAjRpDtZ5/xPQ8BBb76ivh15eXAkCFxHyZm2BuQEkUPBPmhzqgy54eSH6fD3rSqjvnM5fy0tycYr6+hAgDGKoRta84olR+Aj/JTQoo8RBRbMBH2lpDtYFB2HtRr7bCo9iYKHjgDo1AgHUc4N1eOSkg49Ewv5wfgQwJotcwY4V92htzxVJ3C19zLnnhFFXpgmOsEj8dUjplADCShI0lD3xYs4HseAgrQkLcxYxQhRNYhyE+6QGudn2xj8qNUfhKqlqQV9uYz9oicCD1TKj8ZCLIjIKWlABA5U25g25bZasU1j1K8DKbgbSG89fVyhykujlZ+dDqTLddcWdpci/wkXPYrjaFMgjOZBG5bDhugT7wAdiSAh/qiVWkuhm0niVeskDvbbGu1mwPxAhCpNAryEx+S0JEcNoyMYVu2JHXaR+pAkhIucU0hyE+6QCvsLTt0WWIoP8Fggkq/VtWxGLOmtpWKVYRAhRUvbwAewPkFNxXtVl7DXDQbEhC7lR+z8fKATeSHqoy5uUB2dqTyEwzqSnm2tFtR2jxKdQoERMhKIqBjiFYoUEuLLrG0hcyrVKeotcoYKgHyGGYcNuyoAuJjrPwoc504hNyF2x0j5M72MMfiYgAQYW92QFnuOkkmoYqLgVNOIZ9F6JsLsGYNsG0byU0YNSqhQwnyky5QhL2ZJT/KsssJ5f1ohb1RtdKEI26X+kLbkJcVepM5nXyvIF7KZuaimWnOTxT5aWvTzT63lfx06AAA0bP0TpYYV5RVR2ZmtG0R+hY/FDPxyMiIVH4kSXOxT8Cm+6pU7ZQEJNNYvXZSfQkXjOFAQGJNHjlZaS7W2O1ouzkoP8qwYbHIaQLo0YNMnNTVAfv38z4b0xChby7Cu++S7ahRkeFBFiFJcsED3WpvgEx+tm9P2udekB+tdX5ikB+fT1Zf7CIBstMS+psB+bGFgCiIFxVbcrODMQ9st/qiFHqYkB8j5QeI6TzYSX7kKlEhx4UF+QmFGobJdpbXhoOnORTqMaBBap10SKkz6vMBubnRBIRhyWc57M2cbUfKbMew7WyuE0flx4LthMQFjXYDQvlJCLm5pHwwkLRFD5JEsEpdzJlDthdfnNBhDh+WA1AqKw12rKggk32SBHz/fUI2eSG9yY8kaa/zkxO6LAYExJaKbxpFB7xe45ARwGZnuKREVn5yQuSHVdibQnXK9AXhZZFvZKT8AM4WuaDtDjnJlPjFuu5O5FmFHfQcnw0HT3OoQoEilB/A2UUv9RLQWSg/ra3ym1IZ/hWDeDFRnVxM+hzJdaKqk4nqggm9szTaDQjlJ2HQmfQtW/iehwWcfjrhbbW1wNq1vM8mjbF9O7BqFUnCGjs2oUPRkLeSEnk5BE14PGQhVSCpCLsS6U1+9NYsyAm9SQwGc1udYYvKj90EJKz85BgrEICDqpMF23YrPx6PhAy6HliMhSETsq1SfsJtzw21Xeei2k12AQX5yc204eBpDt08iJC8xkL5USegmyQgthCv0AGZEi+l/QgCYs62M7lO4GY7M9NY+VGGatvV34TyYyOoI5lE5CcnBxg+nHz++GO+55LWoIUOzjqLlLlOAKbyfSiSkLArkd7khzotGRkRL28z5MdW5UeZ8xPDcQCcIyB51AlnqDpF2TZBfhJyHrTKPfs88jRHDPJjZ9ibrLiF2q5T7MGJsLew0phrR/WMNIee8hOjZL5thSw0bPuyjEN3bbVdUAB4vbIjHqPdtiggwaA8EChVp1B3ZqY6xal42RJ6FlFhz9h26BUXth83NKqjAiHlp73dhhV70xRJmkB+wQVkK8gPR9CQt0suSfhQlshPEhJ2JdKb/CiKHcDjkXN+ckNvEqfJj0bOj9cXO+TOqbC3XCpzOm1bS/nJVYT76bxA7bYtr60ELuQnSvVyMlFah/z48oTykzCU4wgU1zbbeBLFFhKgp/ywICAKJxywTrwSmsRQnjiPQg9aqpNJ235/gmu0aZb4hqFtpX0nlJ8MhMbshBqWxkhSR3L0aLL96it5GBRgiAMHgC+/JJ8vuijhw9FiB4L8pDp0Zk2z8mKTn4Sd4dZW2ftVqhBZDHJ+2tvlf1aGvdHQCB4hd4qwDEdLPmsqPwCys8kvTub86Co/DFaG18v5EcpP4tBTfmIUTnEi7C2KgDjZp3SIFxPVidrOzASysxUExNi2Ezk/ZpUfZREmu657uK/FsA3Yf89l2xJo1LAIfYsTVPnZujWp1LPu3YETTiAFdObP5302aYgPPiD9ZdAgoFu3hA+3ezfZdu5sYmdBfpIYqipN4cGchfJDnVGPBygqUoS9xVZ+EiYgyimaDh1kJzzPPPGKmwRIkmbYW26eR96HBQlQr63EWPmRJGXODwxt2xIuo8r5Cfe3PDvKFqY5FOOIsrJ1LOXH9oIHUCg/JomXnba5hft5PKYVL1tst7XJz6oF5SdUkC9x+1r5RhbCpe2651GqUwz7Agbo1o1cyOZm2QNNEojQN46gJa4TrPJG8eOPZEuLDxqCkp89e5LSf0hv8qMKV7FCfhJ2hpWVvzIy4lJ+4rZNHeGiIsDrlZ3wfAYhdw0NsuetDLnL85DAdAP7CdsOBOR7rlZ+YpAfu0tdK83kxVDcqO1gMIFF1HXD3oTykzAUjrhy0paJ8qOX88PStkXlx4lwP1nxMkd+2toSiNBSFnpQkgALY7ddBERWXxhVCdXI+cnM9MjSkyA/8cHnIzIKkHQz6ZT8zJ2bVKJV8qOxEfj0U/LZJvKzcyfZduliYufS0rA/ga1bbbHPEpbJz3//+1+MHTsW1dXV8Hg8eJcyzxAkScLkyZNRXV2N3NxcDBs2DOvWrYvYp7W1FbfffjvKy8uRn5+PcePGYdeuXQk1JC4oBnJJ0iA/Tpa6VigQgCIB3YTyk/BLTGU7rPwUhGy3t8sERYWEVSdqOysLyM2NzPkxSUDitk3vN8Bd+YlY3ygG6bSlUpNewYOcTEPbTiClxhBANwncrPLjSM4PS9uqXCceqpOs/JibQAESaDu1nZcH+HzRtp0MPZMkzWILZpQfu0mnlbBhJ5By40iSFj0480zSr/fuBVau5H02aYS5c8k4d9xxwIABthzSkvIDJHXom2Xy09jYiIEDB+LZZ5/V/Pujjz6KJ554As8++yyWLVuGyspKjBw5EkcVb7qJEydizpw5mD17NhYvXoyGhgaMGTMGAR2H2zEowlWUyZtZ+bFnsRJ2hvVKD9NFJ50Me1Plf4QJSIGiOzi12KeSeHk8kaFflIA4VfWMXvOCAiAzU5v8xIjXt6vUdXh9o0yF+qJzz71emQDZdc+jSl3rXHMnkFJjCKDpjAKxq0ZSZ1QpJFiGngJi0rYTYW9Wwv3iDuOMletkEHpGH/W42x6n6gTYQPxaWuSHV5lvRG0bVFxzTnUCF/KTcuNIkjqSWVnygqcffcT3XNIK//kP2f7sZyR9IkE0N5P6CUB6kB9f7F0iMXr0aIymJT5UkCQJTz31FO69915ceumlAIBXX30VnTp1wqxZs3DTTTehrq4OL7/8Mv79739jROiJmTlzJrp06YIFCxbgvPPOS6A5FqEIe4so25kXm/zYpvyEnNFwDka2eeXHFgIChfJTqOgOLS2RkoPKtl0KRLjUdR6cV3501LaImUsTyo8kxTnWaCg/Ee02ICAFBeQ+JXzd1W2ni5wyJD8pNYYAcSs/oVTDCEEyEduAMt/IOHQ3tLs9ti3mG1Hbfj953MI5b/HYtlhhj55uS0sCpDOW6mRgm7Y9YdtA5NpKWYoBqa1Nc4VCW+65UvkJDRk+HwAve/KTcuNIEjuSY8aQ9JN33wX+/GfeZ5MGaGwEPvyQfB4/3pZD0pC3/Hz53RQTSdxnbc352bZtG2prazFq1Kjwd9nZ2Rg6dCiWLFkCAFixYgXa29sj9qmurkb//v3D+6jR2tqK+vr6iB9boLdmQX4WNaz7r86FvYVe3m1tzpV8VjnCETk/NHuVEQHRVH6cSvzXC/3yxrZNyU8wmMD7nZIfZa6TiXYDCV53ZZEJNdmmqhND8mMEp8YQR6GYRIlY+DGHAQHRKzoQQ/mx1XZUvpGxel1QIKf3xW0/zkIPitO1zbZZxctW24WFEbmi4XYb2E/Ytk7IHS/lxwhJ54sASRv2BgDjxpFneuVKYPt23meTBvjwQ/LO7tEDOOkkWw5Jyc+xx1qY3BXkh6C2thYA0KlTp4jvO3XqFP5bbW0tsrKyUBJyfrX2UWPq1KkoLi4O/3QxlY1lAoqZeGXyqxnyY1vBA/VMPH2BGth3KuzNjPpim+0EyE/c62QYKT8m2w3Eec8DAdlxiaiwF9s2kCD5aW6WL5he2BvDnB8jODWGAAwmUTp0CDvCXi/gyTF2COnsWkLrY+gVHTCpOjU3J+Cv6ik/MYiXxyM74nG3nf6jxXA/wIbrrruuU2zilbBt1TWPKvFtYD9h283N8qxJYWHkAqsuIz9J54sAsiP5ww+ImEVJAnTsSHJ/ALkAmYCDoCFv48fbEvIGyPk+lrq0ID+R8KhuhiRJUd+pYbTP3Xffjbq6uvDPTkpRE4WG8uP1Ahm5sQfyhJUflfpCTWXnK8iPU5XHdMLerBAQu2xHkB8a/2KCgMRFAvQqnpkgPz6f/H6Pq+1KR7u42BLpAxIkP7TdPl/4IkZVe3OJ8kNh9xgCOOS4JDAbbgv5UT1PZitWhnxnAAkoATq2mRIQ1URGrEIPttjWmUThadtnouKabba9XqCgwNXKD0XS+CIA8Tqzs8mDRD3RJMIll5DtnDl8zyPlcfSoXFf8iitsO6xS+TENSn727k0weZU9bCU/lZWVABA1a7Jv377wDExlZSXa2tpwmA6kGvuokZ2djaKioogfW6AxYxsxkJsoOpAwCQg54tSpLSjMcL7ks07YG5O8GxUBsUK8lMnKcdk3k/PjlNpHbeflAVlZbJUf5TUPvdTDIX+57iI/To0hgEOOS1OTPBuumEQx06eo+qEU5ixD0aeDQTkcNFbIndcrEyDbHfEY+UaA/SRAJl4cbXMgPxHvrRhFW2y17fG4WvlJOl8EIO/9nj3J5yQMfaPVlhcvBvbv53oqqY0PPiC+Qu/ewIkn2nZYy5XeAPIS69iRfE6ycte2kp/u3bujsrIS8xVL/ba1tWHRokUYMmQIAGDw4MHIzMyM2KempgZr164N78MMGgUPzM5i2Z3zI5MfBiWfVcQrHuWntTVOZT6BsDelfdbKD5BgyJ8ixBLg225A0fb8UF93CflxcgxxxHGhEyheL5Cfb2k2PGH1RZI0F+0FzK1V5pgjbqJgjGMKCEcCErYdCOguFeBYu00QkIQLbOgpXi5UfpLOF6FI4jCibt2AQYNIXuz77/M+mxTGm2+S7RVX2BbyBsQZ9gYkbZ+1XO2toaEBWxUMb9u2bVi1ahVKS0tx7LHHYuLEiZgyZQp69eqFXr16YcqUKcjLy8PVV18NACguLsYNN9yAO++8E2VlZSgtLcWkSZMwYMCAcMUVZlCEq9CZV7MDud05P2HyUwDiDDc1uTLvRp37QmevE7VtperZgQMOKD8WyE9c91xFfpgqPyqyK0lyLQ1fPnvlJyXHkKIiy7PhPh/JWz96lHQPOoFmGk1Nsuetyls0k8vVoQN54dnlDFP7WbnmE//tIgHUdnYeP/ITtk3ta1TLdFT5Mak02k76OCk/KTWOUNCiB0nmSFJccgkpejBnDnDDDbzPJgVRVwfMm0c+21TljSKusDeAkJ8lS5Kuz1omP8uXL8c555wT/v2OO+4AAFx33XWYMWMG7rrrLjQ3N+OWW27B4cOHceqpp+LTTz9FIV1YAsCTTz4Jn8+H8ePHo7m5Geeeey5mzJgBr9cbZc9RKMPeQr5fVhYiB3KdusZ25/xQhzo/H5bybuIqu6yybcURz8oiLzu/nzjidpGfZFJ+7CA/VnKdAJvaHbrmyklpX37sdtuNlBxDVBXPzE6idOggkx/LUOVgtCmid8yQgIScYZXqBCjITx571YmaMmPbbhIQZZt+qUF+HCN92XA+x0yH/ES9MxkhpcYRCjqLnoRhbwAhP3/+MzB/PkkBsTMqUADAe++Rh75vX6B/f9sOK0lC+YmJYcOGQTKoM+zxeDB58mRMnjxZd5+cnBxMmzYN06ZNs2rePgSDEaVa20MfI2KnATLCZ2VF/btjOT9U+QFMVz2j7514bVshIB4PsX/kiD0EhCn5Ub28w+WeWeT8mFF+YihegL2kD+AT9pYyYwgQMYECWHNGAeIM79wZp/qiysGgtjMyFIUsnCIgStVJTX5MLBJtdwhWPMQrbtuqYgth2zkZZICUJGahZ2HiZYKAKO93XJNmetecE/lJqXGEIkkdSYp+/YDjjwc2bSJV337xC95nlGJ44w2ytVn1OXxY9kmOOcbiPydpn3Wk2ltSQLm8uF7OD6A7mDuW82OC/CRc9cxM6JlTuS9C+QHgjnYDgDfPXTk/SQdV2eMIZzRGAjqQIAFJ0Bm1xXYo10lpPzufvfITtl1gLtzPEds5Hubqi5V7Tm23t8cp9CZAvARMgoa9bd+eQBUUfvB4gKuuIp+pny5gE2pqgE8/JZ9//nNbD71jB9lWVMSx6LQgP0kGOvWWnQ3k5Gjn/ADOkB/l4hpxkJ+Eyi63tMiObhylrsPnGI9twLjMttPhXwnm/CTUbhfl/EQoPwWxVScBA6iIpaYz6pQjnqAzapttjycix9+K8hOXbY3xM0r5aW/XXSTaNgKiUvvM3HPHiJcJpVG5uCwPsi1gApWV5EYFg2S9nyQEJT/z54uqb7bijTdIvzj9dLkqoE34/nuyPe64OP6ZnsuBAwmu28AW6Ut+VDO2EfHLGRkhjxgx1ZeECIDXSzKeYS3nB0jAGaa2MzLCAblWlZ+4bQeDUSEjblBA0kb5URHOjAxBfhKGDvkxG/aWUBiUXvgVR9UJYEB+lGNY6MGICrlTn5RdtpX2Ewg9q6/XLQhnDNU9t2I74cVldUif2b4uYAIej+xMJtlMOkXv3sDgwaR/v/UW77NJIbz2Gtk6EEtIyU+PHnH8c2EhIe1AUvXZ9CU/RonKQMzBnCo/ra1xvMSow9ShQzjw2oryAyQQeqZ8gWVkQJJ0HHEn8k/q6+XZ2ASqvQEkatESNBQvnqWuua3zA2DPHvJrVRWQkR9bbRMwAL22ZWUArKsvtjijcagAgDPECwCyCkL5RgZ9yjYnPCRlhK87ta38UoWEyE97uzz7EYcCoiwQE9eagG4IdRRhb84iySu+ASL0zXasXg189x152GzO9wHkJXriFpSSMPQtfcmPyhm1Sn6UeTeWQ980Sg9rkh+DF0ncIViqF5jSP3E854fazs0N27GqgNBCPXYoXkwXOXWD8qMiP9XVkEMNm5vlHDgB8zAT9ubSnB8niBegICCM2q20H875MbCvbLflLq9cEDMOBSQrS37k7FRfrPa3hAtsxGFbwCSSvOIbIC9Bs3ixXEVMIAH8+99kO2ZMxHp9diEh5QcQ5CepoFJ+InJ+gJgEJCdHLgJH/R/T0CAg9CXseNibTggUwCDnR8NpYUYCNGaL4835saw6Ae7I+Qld9927ya+dOytsA8JxiQc8w97ckvODyPHTk8NWdVLaz8rJkAfmGMqP35/AxFVREQlbBkMCohE27IZ7LsLebEYKKD/HHAOcdRb5PHs233NJegQCwOuvk88Olc8T5CedoCpRG5HzA5iKn66qIp/pTLpp6FR6Axg4wzplrjMzrZOAuG3zID8qJxWwTn46dSLb2lqLtgFj8sNqnR8j5QcQeT/x4OBBsg1dWzeEvblGdWpr05VWnFJ+zLQ9Pz/MW6wTEA3bzCr81dfL19OIgDgdbijC3pxFCig/gFyQ7NVXRVBBQpg3j1R6KysDRo+2/fCtrfICp3GTH0rYN22y5ZxYIH3Jj07BA7Nhb0Bo5hzyTLppqNQX6tDm5YVeyiZeYgmHnmkVHACcDXtTOeGBgHx5Ha/2puG0UPLj9cJUu2n9+127LNoGzIW9OZFn5ffLyQWh6x6h/GRmyiWgBPmxDheGvVlVnWx3hJVqYoyiA8rCbfHaDgTkVEKzif9xtz1B4gXYYDsnJ3yN3dDfBPmxGZT87NqVwFoa/HHFFeT9tn498L//8T6bJMZLL5HtL36hueZkoti+nZDTggJS6jounHAC2W7alDRMN33Jj9mCBwbOcNzkx2iBU8DZ0DOdsDcr5MeJfCPuyo+Jlzdd+Tgu8qNT4ttxpU/p5YQ8nwjlx+MxRToFdKAqeOAaAuKG/A8D+0VF8iKbiaovShOOX3ejfCMLC9vabdvxe25QXlyEvdmMsjL5HtN4pCREcTHws5+Rz6+8wvdckhY1NcCHH5LPv/qVIyZosYMePeJY+JiiRw8yi3z0aByhUHwgyI8qcZSJ8qN6iUWUuQZMOcN0bLRcR18n7I1WrzNjO1QvICL315JtleoEMMz5UTgO1G8tLjZnmyo/Bw5Y5Al+v3zCRspPIBC5CI8CtN1tbRbXvqONLCoKl2+PUH4AU8qTgAYkKYpUu46AmFhjqKFBt9tZtp2djcjZSZ22Z2TIxUsSJQERxRacDjeMFfbGmHgxs03/QXHjRNibQ/B4Uib07Ze/JNs33khqEYsfpk8nfsEZZ8jqis1ION8HIIMALRW3YUPC58QC6Ut+jNb5AZiSn3iUn2OPJVsaqxmv7XiUH6qAJGqb+tpZWarQMyfCvzSUH3r+XbrAVLs7dJBJoqV7rvRsQ/1NU/kxsK+sLmip7RrtjiI/yopvAubR2CgPHAmGvVmeSFD+U4LV3oAEHHGtqmMZGfIsEgMSoCQ/EYtUO3HdjYotsCIgNqhOcU9cKQrGiLA3B5GEORRaOPtssnDm0aPA22/zPpskQzAI/Otf5LNDqg9gE/kBgD59yHbjxgQPxAbpS34SXOcHkJ1HyyqfTs5PPOTHchlJle14lJ+EbatUJyvEi6pOiVbYA6yTH48nzrwf6rTk54c7WETblWFCOvazsuSZ8n37LNjWuOb0UlRXQ3ESEOTHKmixg+zs8DW06oyGouVw9GjiuS+attvb5YQYFXw++56nCBUgfBIwbBTl43Hb1lDtPR6YUr2o7UTVa6V9syQg4XYnYJv2Nztti7A3B0Bn+ZNkFl0PGRnA9deTzy+/zPdckg4LFwLbtpFBmsYPOgBKfuJe44dCkJ8kgU61N7OlrgHZeeSp/FgmIKqwt3iUH6XqpONXGdtOoNgCVZ1qa+MM/1IoIPTadekC0y/vhMgPnfKFSvnxeuWOZ9D2rl3JdscOC7ZV95sS9bw8xcy/ID/xQdmnQsHS8YQh0cpjlEuZQlNTVA6Gpm3A8EEpLydby+Gzqip3EU44YKrtCdvWyrNiZVsxjlglAXbattrf4ratmjSLx7aABfTtS7br1/M9Dxtw3XVkeFy0KGn8YnfgxRfJ9uc/jwz9sBm0OnXCyk+SEfb0JT+qsLdEc34sFbhQOaTx5PxQArJrFwkJtWw7AeWnuprM6LS1WVQhYoXcmUi8r6ggpyhJFsPuVLb9fpJLCISuJW13W5sho7OL/MRD/OIiPyqnRVnsIJzcKAoexAdVsQPA+kx8Rob875YcUrqzQhK0WnQAADp2jMN2MCg74qHyQPGQn7hsK/8hdIB4VCe7bAcC8vhrNuTOLtuA9f7G07aABVDys2GDxRlG96FLF2DsWPL5uef4nkvSYOdOYM4c8vk3v3HMTGurrPwknFIklJ8kQYLr/AAy+WlqspiwbIPyU1VFZozb24G9ey3YtqHaW2amrHol4ojHQwA8ngRJgEIBCQZJWyoqEJl3Y3DP7SA/waDcRCuk0452R+X7KG0L5ccaNNTEeBxC6k8eOGDBNp116NgxSnWKUCBi2I/L9uHDsscfkhJotw0/RiZCz+KyDUQ54rq27W63hm2liYgQVgbtVtrPzoapaAWlbUsTdoL8sMVxx5Fr2txMahEnOW67jWxnzIhzkfB0wwsvkDF22DDgxBMdM7NpEzHToYO8bmXcoORnz544K/iwRfqSH7Pr/Bi8xHJz5SgA06FvkmQL+fH5ZAfWdOib0nYC1d4A2RG3FHanY9sK+VHatvROUF1zqhodc0wof9dE0QG6v/L/TUFFfqJKfCs/MCI/4XwfpW1BfqxBg/xEOOImnFEgzlAkDWeU2s7NBSFEdCbH7jAounNxcdhGPAQkLtvt7fLzlAD5iTv8S4d4hc0ytK20H0G8TNhubbVYOMXAdk6OOdsCFuDzAccfTz6nQOjbueeS5hw9Cvz737zPxuVoaQH++U/y+fbbHTVFu1bfvgmUuaYoLpYZVBIU6khP8tPWJjt6CRQ8AOKo+KasEJUA+QHiyPtpaoqyHS8BiSvnyIaQOwDo1o1sEyEBEcUOAPKyoYt92r3QqYr8KEt+Rl13AwJiB+mjYW8Ryo8gP/FBIwdDs5CF328YuhJXKBLdWbEqXSIKiF3Ey3HbVC7JyAhf9yjbFhQQu8iPz2d+vTCl7UTVF6sEJD9fftxtveeC/NiPFMr7ycgAbr2VfH722aRZB5MPZs8m41yXLsC4cY6aWreObPv1s+mAVP1Jgj6bnuSHMpWsrITW+QHiqPhGndHMzHCSTzw5P0AcBITa9vnCxiIS7520HQjIUmgs4hUIyCRNA5YVEA21LYr8eDym7rkd5CeqxDfAN+xNkJ/4EEv5cTL0zGYCknS2y8rCkxXMbLe0yDNVNqhO7e1Afb0F+zYRkITCLBVkW3OtMkF+7EMKkR+AFD4oKCBpTAsX8j4bl0KSgGeeIZ9vvTW8Np9TUCo/tqB/f7Jdu9amAzqH9CQ/27aRbffu4RdoVM6PycHccsU3pRMe0hmZKT8a1amYKT/KRS1ikZ8Y9i2TgKNH5RyFkKMaUemNwsJCp3v3Wqg2p6P8hAmnSdu03Xv2GHLDSGjkOgGqsDeT91xABeqMUm8W8ZEfu8Le6PPkePiXGfJjYvx0zLaFdh84YCGfnNrOzAxHDMRzzfPy5Gc/EdKpnCOyQn4cue5C+bEf1COl0/NJjqIi4Be/IJ+feILvubgW//0vsHIleahuvNFxc7Rr2UZ+Bgwg2zVrbDqgcxDkJwRmYW8a6yUwIz8atuMpeJCQ7YKC8EXWtR3DPg17Mx3+RQmAYj0WqvzQdkTYN7BdXi4TZNNqHw2PUpGfcLtN2u7UiTQhGLSgPKnyrITyYyNopZFOncJfRRD68GAC14ee2aX8hPu0y0PuKAEIBCwsNqoku6HJo3hIHxBH25ua5IFDI98oHuVHkB+Xg8YipUDFN4qJE8mj8/HHSeEfs8cjj5DthAkRVUSdQGsrsHUr+Wxb2BslP6tX23RA55Ce5OeHH8j2uOPCXzEjPxrrJfAkP/Hm3dhpO+wwKUPPTCggpst8qwgAoBH2Bji30Cmtex9aRSzqmpu0nZEhX3fTqpeiv0mSjvIjyE980CA/EQ6hyVBKpQphGjaTAJ7E6/BhkhbF0nZ2trzAq+nrbpNtII7rrlHaPFHyYyvhFeTHfvToQRySxkaLsc7uRa9ewGWXkc+PPsr3XFyH1auBuXPJi37SJMfNbd5M/KeiIpU/kAgoi6qtjaOcJVukJ/nRUH50c35ikIC4lR+FI84s50eDeOmqL36/oUdCbR84IJ+/IcyQH6X9GGW+fT5yeqbUF412a5IfJxY69fvlwNpQycp4w94AiyF/khQR9nb4sHx4QX5sQCzyA7hDATFRdpkH+VFE35pf4NUNBMQG25avu9K2SnWyUmwBiKPdjY3y2KAXZkltB4MWmKyAITIz5Xik777jey424o9/JNs33kgZTmcPHnuMbH/2MxtWHI0N6pb062dDpTeKwkJZVHC5tJee5MdA+bGyzg/gjrC3gwctEhCN6lRRyg8Qc2V6OnNqquyzBgGJVwHxeuW2mwp9U7W7pUV+8VtVfgCL5GfzZsKs8/PD8Xrxkj7AIvlpaJAdkdLScB8tK4tMRxGLnMaBYFBOAtcLewOcW/dFuc4PIk3EQ7yUUVUxYRMJ8HrloSjpCIhNtk3fc42CA8zbnZ2teEnFl98mYBE/+QnZphD5OflkUvo6EAAef5z32bgEO3YQNggAd93FxCTlJrbl+1AkSd5PepIfB3J+9u41OeFlI/kpLrZIQMyoL8qXmJ3KkwHx0iQBMVQISyRARbwoacnLi7gUzpAfOgAMGBAursFM+aHXPCsLyM3VLnOttC2UH/M4dEiOuUzQIU0o+T7BvJvCQnnM4xH+ZZkE2KR4OW47CVSnuIhXaIo4GJSjJQT5cRCU/KxaxfMsbAdVf/71L4uLtKcq/v538j4591xg8GAmJlesINuTTrL5wEmS95N+5KehQR7MjXJ+LCSu+nwkwqi21oR9Kzk/ra0xC+JbIiAGYW9hRzwcQwFnyI8NYW9AnCRAo9JbhNzrBPmhAwAdEKDTbpOhZ3GRvlB8kWaxAwu2BRSgb+zS0ojCBvFUT7ScfK9Rcllpwooz7PHwVUB4kgA3EBBHbMcYv+xsNxDq6ybXSROwiBQlPyNGAKecQsbLv/2N99lwxq5d8qKmd9/NxKQkyeTn5JNtPrhQflwKGidVUhIuVwrEv85PRoa8qK2p0DcNEhCV86OcRYtRT5mGbTmivjhMfuKtegZYrPimIn2a+T6AaechLuUnlO8DMFR+zJS5BgT5iQca+T6AhkNKb7LBtbWcfE+dUZ8vXEFQ07bJ+2pJCZAkc2W2TaqJjpAAk8+So7btvOZmbZsMX7Wz3RH2TfR1AYsYOJBst2+3UJbQ/fB4gIceIp+ff95C2kAqYsoU4uedfTYwfDgTkzt3ksfa54twTewBPeData6uUph+5Ecj5A0wyPkxMYtlKe9nyxayDXnewaBMfqKUHxP27SIg8TjiiapOhgqIg8qPZplrwLTa54jyY5H8/PijiXHFzAKnyhMRTot5WCU/MZLyaOScKfVYo+QyoEFA6GxKjGQe6tOaCj+pr5cHSw1nODxvQ9tt0jYV42PCDAlIItumQ37MhDlS221thjHYtK/ZYdvrVazDSPubqQRUAVMoKZFfVCmU9wMAI0cCZ55JXrcPP8z7bDhhxw4S+wcAf/mLjZUHjLF8Odn27x/pbtqCnj3JoNTUJPu7LkT6kR+NYgeARtgbZSI0vMQApslPW5usBITiOpXvyDD5CTMw8CEgTio/NqlOieT86Co/Jm3T/6upiZHnVVcnn6CC/CRCODt3JmpjW5sJR1lFdmMqPyJcxTw0yE/UopOAaQJiaQJFI/kd0HieTBKvuGwXFEQ8uFF92ol2+/1yWThF22nzHLUNaF73eNtNn0HTa4UZ2I7qazHsU9sNDWT9Z9O2jZQ+wDTxE7CIFCx6AESqP//6l4V1+1IJU6aQl8bw4cDQoczMOhbyBpDZkEGDyGfKslyI9CM/MZSfMPmhIXF1dTEPafolum4d8VpLSsJxW5RbeTyq9W4YERBmyo+ZNYYs2KZhbzt2mFBAdJSfeMlPRQV5voPBGARk7Vqy7dzZNtKXmSkrTzGJn1nlRxQ8sA4N8qPkGGE/1AkCosNio8JnnSABOrapiSjbdrZ7714Sduf1RjjiTGzrLJKlS7xi2KbPcG2tyUI5Graj2p2dLefdGNgvKJDDLE21vaZG13bE2C2UH2eQonk/APH3R4wg/tf99/M+G8bYuhV45RXy+cEHmZqmnMQR8qM88LJlDhlIHOlHfnSUn6icnzjIT8xZPGV5jZC8qXRY4km+d0PRgZ07LRAQm2wfcwx5z7e2mghbUSk/yoIHETBpOyNDvueGoW8a+T5AYoQTsKB66ZAfkfNjAwzIj3LugiUBidcRd4R4OUn6qqpkJx8a7Y7Ddoy6MiTcj95DmuQJDQKitG1w0IoKwuFiTqBQUAKisB11zT0ey203FbprQPqUYpNQfhwCJT/ffsv1NJzC1KlkO3Mm8L//8T0XpvjTn8jMx+jRJP6PERwtdkBBDyyUHxfBbM4PJT9Hj8olbXVA3wkxX+C0xylKGUZVeqOwSH5iEpD29igFRJISKzpQXS2HYMWMH7cachfDEc/MlK97TBJgVvmxsEo5nbk1LDGuLHOtQCKkD4iP/Pj98j0SOT82gF5MjTCkiIkMRspPICB32yhHPIYzSvuyZQKiQLyqk6X8Oauqk0nC2dRkYo6L2u7QIWLWQpdwKmMgNeD1WiiUo3x4jVQnpX2HybYm+RHKjzP46U/Jds2alLy2J58M/OIX5PMdd5iYiEgFLF4MvP02caAefZSp6e3bSfRwZibJ+XEElPysXOnaRY/Ti/xIkvmcH0UluFiB0aZfJHTmRlFYPVHy07kzcbTa2mIoIOvXE3ZUXAxUVgKI9PHjUSF8PrnthspTU5N8cai3g8SIF2Ch4puCeNXXk0lcIH7lBzDptGkUOwAYKj8Kta22lnR/ny8iYohAkB/rMFB+HHdGNcKQNEPuLCo/dhCQeFWnQ4dMdD8N9UNpwmq7c3PlaNSYbbeqeJmwb/qemw33U9o3GXZnK/kRyo8z6NyZ/ASDrp5JTwRTppDu89VXwFtv8T4bhyFJwJ13ks833OAgA9HGf/9LtoMHqxY7txO9exOntqkJ2LjRISOJwXbyM3nyZHg8noifypCzDQCSJGHy5Mmorq5Gbm4uhg0bhnXr1tl9GtrYv5/cDI8nqtSX5iKntGfEmBY09RJrb5cTFm1UfpQKiCEBURKv0LS00tmIl4CYCrv77jsycHfqFOG4JFLtDTBJAtrbZfJaWhpWakpK4r/mgAnyI0kxw97iUbyA+JQfnYihSNsuKXjg6jGEQiOG0NAhNOmMxktAaJ+KCLmzqDqZWqjZ5rC3Dh3k5yBm2LBZ4mXBCTdNQMyqTllZcvkzu+55jHA/TfXFLrJ99Kg8fhqF+wGm7zkrJMU4YhannUa233zD9zwcQufOwF13kc933ZXi/PnNN0l8X34+qfDGGIsWka2j9RW8XtnPdSlhd0T56devH2pqasI/axSLHT366KN44okn8Oyzz2LZsmWorKzEyJEjcdRU2ZkEQUPeOneOorxROT+AnBVqkvw0NMiqQhTWrydSS3Ex0KNH+GvNFxhgPwHRWM6XDjA+n6rddtumxGvwYM3SvI6GfynXRujQQT/kzaLtmI7Lzp2k3/h8QJ8+EX/SLPRgN+kD5MpYJSX6xQ6UtpubXRNz4NoxBIiM81R0JDuc0T17TOTPxUi8Dz9iJm2bLuChYzsYjD/0zONJnIAYhtzFuJimbcdQnRxV+8zmeCl/sUt1ou0uLCQ/RrZNtpslXD2OWMGpp5Lt0qV8z8NB/OEPZDjdsYN5/j87HD0KTJpEPt91VzgKhyWYkB/A9Xk/jpAfn8+HysrK8E/HkFQvSRKeeuop3Hvvvbj00kvRv39/vPrqq2hqasKsWbOcOJVI6IS8ARo5P4Ac+qbLaAjy8+VddV8mlHwMGhRBABJVfoA4CEgImuTDCdsaxEtpP97wL1Nhb9RxKCkBfD5j8hNHzo8u+aEv2T59VJ0q8XA/JfnR5Srt7cCmTeRzjx76Za6VJyJJMRfVZQXXjiEAcQglidzX8vLw14koP5WVZFjw+2MsPhkMGoa9xWPb0kLNGh1J2WWthr0BFkKwrJIfIKaSmqjtRBQQ20mf8hdGxMvNyg/g8nHECqjys3Spayao7EZeHvCPf5DPjz+ecpW9CR58kDx0xx1H2B5j7NpF3OCMDOCMMxw2dsopZPv11w4big+OkJ8tW7aguroa3bt3x5VXXokfQqRj27ZtqK2txahRo8L7ZmdnY+jQoViyZInu8VpbW1FfXx/xExd0ih0AGmFvgL3lrjXIB8CI/AQCcplMDeUngnzYbRvQbHsgIPvZjio/1HZopWzdSm8WbcckPzr5PkDipI9e84YGObItCuvWyUpjz57mlB/lyXGG3WMIYOM4Qhk0XXQphESc0cxMOX3I0CHdt488PB6PZr5RPLYBkyRAkgzD/YDEQs9ihn/plCuMGseU/dkkCYjXtqECYlfeDd1BpTppEi+787zMFrhQ/uIi5ce1vohVDB5MQolqakzGxiYnxo4FLruMDHG//nXMWlPJhTVrgKeeIp+ffVZj1tl5UNXnpJPkwCbHQCvYrVoVU0DgAdvJz6mnnorXXnsNn3zyCV566SXU1tZiyJAhOHjwIGpDMRWdVKuid+rUKfw3LUydOhXFxcXhny6anqsJmFB+4iU/MRet06j0BjAiPxs3Eqe2oADo1Sv8tR3KDyUgurZbWogjDkQQL+WhHVVAqOQauu52h73t2aMzQOvk+wCJFzzIzZWLjOkSP2V/83j0y1wDRMGgaqQLyI8TYwhg4zhCnQ/V/yeaB2FqNp4+aJ07y7klNto29KsOHpT7h0bhkpwcBRdUOuExZqpNqxC07Yp8TUnScMQzMuRBxS71hT5oqlxRJuqLRruVthMJe9u717Aone5sUTIoP672RawiL09+l6Rw6BsAPPMMibD83/8IR0gJBIPALbcQZ+HSS0l5aw5gFvIGkEHmuONI22NMTPKA7eRn9OjRuOyyyzBgwACMGDECH330EQDg1VdfDe/jiVjQhkjQ6u+UuPvuu1FXVxf+2WlYX9gAOsqPMtrHEeXH75c1XFXoF5OcH6p+DBoUMVOtq/xYyD+htnWd8DVrSPvLyyNeoEq/IN7Ef1MKiGo1L7vIT2UluZR+v06VPRPKT7ykDzCheqnaTUm5pvJjYVFdFnBiDAFsHEfo/ykIACBPZMQ7G26KgOg44TRNIVHbho44td2pk6Kqgmw7YiZRWfI5RiilKdv19XL+nqLtra1ykQZFSor9axzRwZU+eCHQex5hO461dgz5Ib3uOrYjJs4s5nlJUow8Lx3byZDz42pfJB6cfjrZLl7MziYHVFcDjzxCPv/pT8CGDXzPxxY8/zy5b3l5svrDGJIELFxIPjMhPwBw1llk++WXjAyah+OlrvPz8zFgwABs2bIlXGlFPbOyb9++qBkYJbKzs1FUVBTxExd0lJ+DB+XZe8UyNPaRnw0biMdbWBihvACMlB+NEtuACeXHAgE5eFDnXa+xsKvy0FlZqupjFohXbq4c+aNJApQV9lTkR+U7EtCcHxO2fT45EiTKWW1rk/NtHFB+ADnfySz5MQx7A1xd7tqOMQSwcRyhN1xFfqiyH3FYC7PhpnLYdJxRaltZod+KbXo4Q9s6BIAOj5rtBmI6w/RZNNXu0tKIwVIZTRExhpoMuzNlu6VFLnChGjg077mFsDePhzxyhnleOtc9kXuekSFPAPHobzzgKl8kHpxzDtlSDzaF8ZvfAKNGkUfv2mtjqJNuxw8/yKXs/vY3nZlX57FxI/D998Tnol3JcVDyQ+truwiOk5/W1lZs2LABVVVV6N69OyorKzF//vzw39va2rBo0SIMGTLE2RNpb5c9X5Xys3Ur2R5zjMohtYv86CgvgL3kZ/9+Hd/VSsEBi7aLi+UXv+YkmE6ukx0hd0AMBUSZ99KjByTJpPJjouABYJD3s3EjmY4uLo5ykAEGyk9bm6w8ha67YcED5cm4kPy4Zgyh0AkFosNEhENoYTa8Z0+ypeORJmI4o/E44Y7YzsyUZfQY9qntLVsMFJAYBKCwUDW0mmw7nQc7csRAPaa28/PlhYFAztXwuse45zk58vDw/fc6OwUC8gBjhXjx7G8uUX7UcN04YhXDhpHt2rUmVhVPbng8wCuvkMnoFSu4VIS2B8Eg8Mtfkmdi6FDg1lu5ncoHH5Dt8OEa/qZTOPtssv3f/1wRVaKE7eRn0qRJWLRoEbZt24ZvvvkGP/vZz1BfX4/rrrsOHo8HEydOxJQpUzBnzhysXbsWEyZMQF5eHq6++mq7TyUSO3eSF0lOTlR5wS1byFYlysgju4lkLUPyo5PvA9hDfkpK5PdOFAEJBskquxr2NauOWbQNxFCeYhCvRG0bzpYr1Q+PBwcPyofVVECobZMvb13yQ4nHiSdGqF0UMZUfE9V8DMnP2rWEAJWUAN27o7FRdsyTQflx7RhCQcNnVQpyosqPHc6o5kx8e3vMqVNafX/r1vgJSNQkuEUCUldnQEB0wv0StZ2XJz8T9D0QBWW7Fc9zY6NcSduxe75nD5lIUUrNIdjV33SJlyQltfLj+nHEKsrLw4V78MUXXE+FBTp3Bl54gXyeMgX47DO+5xMXnn2WJNrk5xM2F7XIHju8/z7Zjh3L0GjPniQ8p62NECAXwfY7sWvXLlx11VU4/vjjcemllyIrKwtLly5F19Dgedddd2HixIm45ZZbcPLJJ2P37t349NNPURgRNO0AqMPSrVtUB6QvHvoyCMMu5UeHAAD25Pwo12yNIj9btxKGlZsLHH98xJ/sUH4AA/LT1iYn/usoP4naNiQBqtAven6dOumsbExndXU9sEjokh/aZo18n5hV7gBTypPpdiuKHRQUqHITlLAQ6ug0XDuGAMQh1Amf1XQILagvlIB8/70JAmJFBQBiEvrjjiPjSH09cOCAzk5WVADAdOiZkoDokgCHbAMmCEgM4uX1xlftzZLtLl2IIQU0ww3ttH3okHwcHZXTzcqPq8eReDF8ONmmQegbAIwfD1x/PZlkuOoqE7l5bsKqVXI560cf1Sy0xQoHDsgVp8eMYWjY45ETjFzGXn2xd7GG2bNnG/7d4/Fg8uTJmDx5st2mjWFQ5lpX+Ymj2lttLXFww+8pZZlph5QfgLyXN2zQICCUeA0cGFEdCjBQfugXJl9iuuRn7Voy41xSIks0ITAJe7NS7AAAQmtA4MAB4n3GSKA3pfyooOQWmoucAqTtSjKkAVPtVoW86ao+SvsukKZdO4YAJLmNZvir+rRh7ouJZ4nOyzQ2kqiWqPXvJEmWOM3k3WRnkwPSVUgjWFkkaAjWzp3EGaaPQgR0bCeqvgDEEd+9m9im6zmytL1okQEJMGE7Yqiwk3hR2yriFQzGKDRhJ+mrqIgaqBNVnVjA1eNIvBg+HHjySeDzz3mfCTM8+yxxY1avBq64gjQ9ojCVG9HQQE62rQ0YNw64+Waup/PRR2TMGDhQJ9/ZSZx/PvCf/wAff+yq1Wv5aXCsYVDm2g7lp1MnQniCQVU47qZN5EWUnw/07h31f7rkh84+mbANGBAQnZwbwICAlJWRre4UcCR0y10rCy2oiETMkDuTxEs37K21VSYhZskPXbDS7zd13eNRfpTkJ4LfZGbK18hE2+k11yw0QQmvqtiBbr4P4KqwN1eDjiPV1VEE1VD5aW2NuWhFVpZ8XzUd0gMH5H5pJuTO47EvBEuS5Fki1UCpSbwAex1xHdsxyY8J23TSy3bbdqgvOi8n+t4A4g89U9rWVBp1X4wx+rpLlJ+UxNlnE0djy5YYC9ylDvLygLffJs/ZV18BEye6fJ1XSSK5PZs3EyfhlVdiTqQ6Dbpu78UXczB+/vlku3y5TmlcPkgf8mNQ5toO5cfrlWdqI6RZ6oj+5CdRYQuAAfmhC7mY7CwxyY9GyJ1uqWta7cZkUqVuuWuDcD+7iVeU7TVriOpUVhbeybDSG0CcWXojTNjXJD+HDskdoH//qP/RXBMFIIOjhbYXF8vdM6LtLS0y+TJT5ppCkB9zMJhEocOEZtljIHFnmH7ZpUvUg+M4Adm/n0gNHk9U23Vtx+GIa+bdSJJ8UqpB2k7bMcmPWdt2kp8YtjMzVSG8Fu43DXWsqyOTKGZtK+27WflJSRQVAbQgA03iSAP07Am89hrpr889B0ybxvuMDPD88+RkMzII66DvdU7YvRtYsIB8vvZaDidQVUX8XwD45BMOJ6CN9CE/BmWu6fIRNOY+DAvkB9DJ+zEodgAY5PxQ8mORgESQH0kyJD+6BMRu4mVFdaIM8vBhS7kvhw+r6lKo8l4AE8oPIKs/hrVnCZTkhyY+h4lHt26aSyjrKl6AZdKpWZ5YSfpCNyZmmWvlCQnyYwwD8kNTxRQFwch1pTEaJsYRQ2dYR4EAZOc1wjZgSUE2ZfvYY6MS5mi7o97xdCKBxmfFa3vfPl3iRdtth22rxEv3mlPbSnlGB/Sdc+iQTqqhDgFR9rWISWVq20SRHmW1OSv9rb1dbpru0hCunppPclx0Edm+9x7f82CMiy6S1//5/e+BDz/kez6aWLQI+N3vyOepU+VSzxzx+uvEPznjDA0flxUuuIBs587ldALRSB/yo6P8KMtcRzmkdpAfAwIAGCg/1BFOhIBs20aYXVYW0K9f1P/oFh2g5OfIkZgLFCpt79ypIAHKNXasEK+SEtlZNNH2wkLZ+YhQQFT5PvT8gBjkhyY7mCA/1dXE8WhrU4g1BoubAgbXHIib/ES0WxnyFvKKYpa5Bly1yKmrsXEj2aqKh0iSjiPu8cgeoolCGvTlpOmIG8zE6xIQ+nAcPhzTNvVxN2+2ZluXBFiwTQ9r2G4N4qVLfug1N2GbXvODBzV237+fEAkN4qV7zS3Yzs+Xn0tDtc8s8bJwzYEYxE/HNm23xwN06KD4A213e7sIfXMSlPwsWiTP3KYJJk0CfvUr4mdccQWwZAnvM1Jgxw7gZz8jYfNXXSUXO+AISQLomr7XXcfxREaPJttPPpFXpeaM9CA/DQ2yM6siPwbv9MhS1yZmsqLIj7LMtAYBoKcGxAh7M2FbSX7Cu1PideKJmhmCuipESYkcomeSBGRkkHde2G/fsIEoN0VFmtMNuiTA47GsemmSAA3yo7M8SySo8mMi9CwrS+Yr4dA3qvxoFDsA7FV+NBc61Wi3UH5sBCU/ffpEfN3QIFeTToSA0DkK2o0ioOOMAvaQgBNOINv16zXSkwxUp5gkwATp69WLjCGHDpGiMREwaLem2qb8wkS7CwrkMWHdOtUfabu7dInK8dK95harRtKutHat6g9KNqYaQ2Peb5O26T2Psg3ovhypbeVrAgBhcrSojkn7AnGgZ0+gb1/iRH78Me+zYQqPB/jHP0gaSVMTERSoi8UVhw6RkzlwgPh6//oX9zwfgFR4W7+ezBldfjnHEzntNDJYHTrkmmId6UF+qOpTWhpV8cggp1PeNxg0FcdMZ/DoTDs2b5bLTKucJYA4GHSiXZf8tLSYCt3o3Jk8ay0tCr/dIOcGMCAgGRmWCIjPJzvWYeWJ2tZY2FVp287wrzAJaG6W3+YhEhAIyCTALuUH0Mj7MSh2QE8N0FF+aMifzaRPFDywCZKkS36oQ5idrXFvLTikNDSa1kmJALWtckaDQdlHTsQR792bdIOmJg0VYtMmeScV7CABeXnyoWlxzDBoux2yDcjXPcq2iXZHES8LhNOU7ajVt00ofUo2Ho/tI0dk5V3V3+j7Jcq2x2NZeRKIE1T9efddrqfBA5mZpADCWWeRoJxRozQmLViipYXcj/XriSP07rs6L3j2ePxxsr3mGpVKyxo+H1HFAOCNNzieiIz0Ij8acfqGyk9enjy1Fc9aP8piB6oy00Akn4rK+cnPl780Ef6VnS37zmECEiPkzlCFSDTvJxHbFklAVMW3774jbKdTp/BNUZYgV60VGAkLyg+gIj/BoGnlxzDsLWrqWxuapI++BULXXZJMFjygKmeahVFYwp49ZCLC642aLVE64VETfhYcwspK8ugFg6rZeL+fqKlAVCGNI0fkcFNdR9wECfB6Zc4e5QzTk9Eo4mGH+gIYOOIGtu1SQOKxHZOAOGhbt91KD8fEdae2V65UBRhQ28ceG7U4mC7pAyxfd4E4cdllZPvBB6bD8lMJeXmk6YMHk1f1sGGyy8EUfj/w858DixeTyfK5c2PMrrLD1q3AnDnk8x138D0XACQUEADeecdUPrfTSA/yQ5OUDdb40VR+PB5LeT+0z69dG7q3JvN9vF6dRTcTISAxih0AMVQIi7ajyl2bVJ0MlZ94SYBBsYPOnTWL7slIRPnZvp0w2uxsHTbtsOK1ejUZjCsqwid24IA8ARy1bowSlBlFrZIrEAYlHz16kJhHBXRnwwF7lIDvvyeDSm5u1DhGu2phocY4YgcBaWmRB0qVI97eLvu5UWsDWXSEBw3SsA0YkgDadjpnEQYDAkKHRl3bSlZq0nbE7vG02+uV31km2t6/PxHm9+9XDbcmiFeUbUAoP6xw0kkkRralhayhkoYoLiYpJCefTMbf4cMZ5wAFAiSR5p13yPvg3Xd1Iz544IkniBt44YUkSpI7zjqL+Bl1dcC8ebzPJk3ITzxlrikskJ/TTiOqQm0t8NJLiEkAlPk+muGhcZac/vFHECf2wAGiOGm8wACT+SfxEK8YC7sCDpOAeIsdAJELnZpABPmhxQ769tVU+gCTyo/FdtfUhOpSaJA+qvpUVET565GgF0aQH33Q+6vxPNXUkK2mqmiHCkGd0X79osJIDW3HSQJorRIAJOwsGCTHUjFo2lV9PocUkPp6+cFWFW2RJIO2x0n66LrMYRiQAEoWomzT+y1Jpt4bffqQZ/PoUVXlRgPbpu65ibbn5sq1OyKuu0H4LrWtOZlikegLxAmPB5gwgXyeMYPnmXBFWRnw2WfAmWeSR23ECEaRgIEAuf6zZpHB7z//IfKTS/DDDyTtCCBFIlyBjAxSpQIAZs7key5IF/JjUOaavpt0SwBaID+5ucB995HPDz8soWlFaKbYaqU3ikSUH6r69O8flahLYUr5iWetn40bycHz83VZJW2SZtvjTPwPOw6JkB8Lpa4BmfysWQNIq43zfQB7SV/HjuQ4khRqn0ZZdVPFDgBBfsyAeodUolDALmcUiEF+DJxwO5zRmLZVszS03Z06aaT2WWz3wIFku3mzIiR4/Xqyra6OirOqq5NzJqPariScJtQXWpm+tVVOtcGhQ3IDNaZOde95VpYcsmyC+GVmyrc1fN0lyRT5MbzndpDteImXCHtzHj//OXnolizRKdGYHigqIkLC+eeT9+ull8qqhyNoayML5sycSZTWN9+Uc7BcgnvvJZM4I0e6ipMBv/gF2b77riI5ng/Sg/zEU+aawmK56xtvJC/S2loPnm24jhAPHc1Rd40fCjvIj47qJEmyf69p3w7bgwZpxpgFAmS2BpDXa4tAnIn/+/YBzfsb5PAkBQkwVekNsKz8DBtGyOOqVcCMD0Ivfp18H0AujBCxPgYFJT/795ty2DweFenUIH2mylwD8oXZt88V8biuBC0rRL1FBUwpP3EQkLAKoazcaMW2RWd0wADSr2pqFO8maluD1OuqH4BlJ7xTJ3IcSVJUcDJhu7hYp2IlQJ4jEwVjMjJk8kXnEMK2u3ePynvx++XxU5OAxKl6hW3v3k0MeL1ySTYFnCDbdPhAMChLfwbKjx19XSABVFURjx8gC2umMfLzSQ7Qb35Dxo877yTCjO3r7TY2EqLzxhtE8XnjDcK2XIT//Q+YPZuM4489xvtsVBg4kMh0fj/w4otcTyX1yY8k6RY8iBnyBkSWuzaBrCzggQfI50fwR9T3O103BCqm8hOnAvLNN0Dr//TX2AEIP9i1i+QIaExkWw57owRkyxagbsk6Q9tLl5J3Y0kJCRXUtW0y56ekRL6GP87fRF7e1dURb2enlJ/OnYEHHySfJ337c+xHuaHys3gx2Wq2mxLOQEBnyfVohEP+NrfKxQ6slrkGSLupQhguXScQRkuLTKo1yA8lCnYQkOOPJ326uVmRxLtsGdmecoqjtgsKZBLw5ZexbdP+FdMJNzkNe/rpZEufk7ht5+bKjMhk263Yrq0lTfJ6dXJfLJIAOgkUtk2ZSL9+UdK8Ybif0rbJdittSxJCg3id7sSdnbYFEsTtt5PtSy+lPeH0+YDnniOqT0YG8NprwE9/Kg/bCWPvXhJXN2+eXHGBa/3oaLS1kQl4gIgsdCx3FW67jWxffNHUOpJOIfXJz759JNFCOU0egmGxAwqLyg9AygoeX7oPh1CGJz36ZTbsDns791zi8+/cCTz9VcgB1iEg779PtsOH26P8nHACiV1vaAAe+iD0xOmE+9GlCc47T4cXWiR9Ho8s6i35IEQaFAQAiCPn5+hR0wrI734HDBwQxKFgB9yBJwzLXP/vf+Tz2Wdr7JCZKTuMJttOwzXnvXWUkL7KygiZx1SZa4BcRBH6po9Vqwgp7dhRk0nSkEtKRiNg0RHOyJAXBv/ySxB2U1ND/qAxU0Ft08mPRGwDct/88kuQGTrKwDRIAE3HMbQdCJhSX5S2//vf0BeUBFi1DVhWQKzYptf82GN1CqhYJJ30fn/zTWjYMSBehw7J7w7N8cyi7VNOIZNg+/aF3onU9qBBmuvDKduuazvNHXFmOO888r5pbAReeIH32XCHxwP8/vfAwoXkVbh+PXEFpk9PMAxuxQpyoKVLybi2YIGsurkIf/0rCcHv2NGFqg/FJZeQmZO9e7mWvU598kPzfbp0icr4Nlg7T0Yc5MfnA/5SRWTox9eO0p3Et5v85OcDU6eSzw81/A57PZW61P+DD8h27NgYtk064V4vmXEBgKd3X4at6KFLvCj5ueACnYNR8nP4sOmZAVpF8c/vD0YTcuMnP8XFsjdjMvQtMxP4552b4EEQM3Et5q/RLqv2v/+R5lRWGuSYWVS9brqJDPj/WViOr3FaVLtNlbmmEORHH7SM0Omna1Yn0YmsJYgjD4I6w4sWQXZG+/XTnKmgzqih7cOHTYVSRtnesIFMHhUUyJnxCtB2axKQ3FxZTbRIAhYvBvx1jXLOjwEB0SU/FlWIM84gt3bz5pC6Qa+76pkyZdsiCejVizz6ra2hCRID8kNtV1bqhGtbJLzZ2cCpp5LP//2vse2mJvmVoNnfhPLDFh6PnNH+zDNinbYQhg4l81Xnnkv67C9/SfyNcDVaK3jjDRKqtWsXmeFdulSWiV2EL76Q/b/nntOovukWZGUBv/0t+fyXv5haj8wJpD75MfBKnFJ+IEn42c6nMBCrcLQlC48+qr0bfa/rdlKLCghAVKdTeh3BURThvuJpmtUM9u8nK/8CJsjPvn2mp0xGjwbOP6sB7cjCpIwnNRd23b2bDEoej8HESUmJLAmZJH4TJ5KZyF1NZXgCd0Q4LG1t8iXUnK1UIiPD8lo/APBT6RvchmcBADff4tF8B9EworPPNlj82WK+08CBZGAHgN/jSQRPinTUTCs/gCA/RqDkRyNJrb5e9vU0HUL6HNfUmH6Whg8n288/B1oXLSW/UA9VBUMCQhceCgZNh1IOHUr+Ze1aYPdHq8iXp5yiKXFQ25qKFyA/Sxb6c0kJEYqWTl9PzrtLF80YKzqvFdO2yTGkQwd5vubTNw6S58Dr1VSwY7ablr4zadvjAc45h3z+ZG6QOFgAidvRsa1LvCxec0Bh+xPIfV3DNiVeRUU6eYsWJ80EbMCVV5IXW20t8NRTvM/GNejUifTnqVMJwZ83j8wf/eMfRIyOieZmEqJ19dUk7PnCC8lzqbHgMW9s307WEA0EiA9I1xN1LW6/nYwVP/zArVph2pIfU2WugfjIzw8/IKP+CB7ykWSQadPkOGml/bfeIp/HjNE5jkXlByC++1NnvwMAePnIpXLisAIffUTsDxokVyvTtd3ebqntT4z9Al748V5wLD5bFB3TRsu7//SnBqQvI8OyApKbC/ztAVJH+m/4E/Z0lmctd+8m7c3J0YnPV8Ni3g8AYPVqPIT70LngCL7/nsjPatBwGjq7rYk4CO9DDwH5niZ8g9MwuzGSzQrlxwZIEvDVV+TzGWdE/XnjRrKtrIzKiyegD1lzs2kCMmgQ8fcbG4FFH4Uk4qFDo/bbv58c0uPRmcTJytJY/dgYHTvKPOujt0Pl1DRKBkmS3HaNeQ6CqNWPjeH1ypMiH70Vqguv0W7AhG3any1M91I1+qM3Q2F6gwdr3lRqW0MMI4ioRGLR9tvNhP116KBZ4ILa1vXBLF5zpe1PPpHQtiJUtVLjutNKeD176kzgxGFbIEFkZQEPP0w+T5kiiKcCXi/wpz+RCdchQ0i0zW23kfF1/nyDf1y9mkyg/uMf5Pc//Ql47z3ZH3QR9u0jz+/Bg2Ty5p//5H1GJpCfD9xzD/n8l784UJkiNlKf/NC4bdVbylSZayA+8hMq2XPhwF047TTi80yZEn1aO3YQYUY3/IsSkEOHLEmDQ/a/hyvxBiRk4Pe/j55sjhnyBhCmQIs9WCBfJ+z9ArfgOQAk9lY9w/LRR2Sr22aKOEjAlcctw2n4Go0owP1PyyyHvoePOcZAcVHCYsU3AMCaNSjCUTx7Helvjz0mV4sFSOoEnVDVzPehiKPdlQUNuBukg/3pjYHhtYTa2uRbJ8hPAli/npDwnBzNEChaZ0K1DI2M7GzLBMTjkZ+R9zeHvHsNZ5Ta7t5dp2Q9oLEQVmxceGHI9upuurZ37yaql89nMIEUh206GfT+qmMhAZrEKxiUE5k1iqElbPuTlRVoRZZunViq2usuHhgH+Rk9mtz3VZvz8SO6kIFCQ22LaTtqxenYOPlk8ro5etSDRdJZhN1ozIzF7Ou03UeOmC4SJGADrr6a3MSGBlLnWCACffqQycdnniFzCmvWAKNGkXEuYoK4vZ28vE85hTxolZWyfGS4Ojof7NtHQvs2bCDv+HffNahc7DbcdBMZq3btkquEMURqk5/2dhxcsBJ7UEV6iAJU9TEscw3I5OfIEfN2Q+THM/ik8ITMiy9GvgeVqo+u01JWJi+eYUWF+PZbPII/IicrgEWLyALEFC0todAGAOPGxThOPCEMK1bgATyIkrxWrFkjL7QFEGeczrY4QX48K5aTkDeQBEe6boXpfB8KSn4sKj8AcPF1xbj4YkJ2brpJTrNYuZJMbnTooLvmLEEc7caqVbhDehzHendh5x4vnnySfE1Fs6wsjQUotSDIjyaC8z7FEpxOCIDGmlm0n+k6hEBcM+KXXUa2/8HlaO/eW7MDU9uGfSoO27R66ydtw7A/q7NmyB213bu3wQK6cRCQCy4AsrIkrG/qju8wUJOAbNlCYvlzcgwmr+KwffLJwDHHSKhvz8OHGKNJ+lpbZQKie93jICDl5fLEyCxcrUu8aBXqmLb37DGdM5mRAVx8cWzbdJ1f3b5eUCDnOwn1hx0yMhAe+F9+WX7JC4Th9ZJoq61bSZEin4/kH590EhlzvnpxLRkA7rqLPDdjx5IOP2oU71PXxLp1ZFheu5ZECXz+uQUfxw3IySHJSQDpu+HSpmyQ0uTn5fu3o0vDetyf/WhU8r2pYgeA/CLZuNF8uRB6EwcPxvDhJH6/vZ2oe0BkyJthpcSMDNkRN6u+7NsH7NqFYz278IeJfgDAH/4gLwb4xRfECa+u1q1HIMNiuWsEg8C336IMhzD5NqKa3HefLJotXkwmpjp1smDbCglYvhynYymuPHE9JAm44w7FAqCwMDBYzfnZu5dcI48H6NcP06aRSJklS2QJmub7nHmmxmKQSsTZ7ly04G8DZwMgk1Q1NZH5PqYUL0F+otDaCpw4+RKcgSVYM+BqzX2++YZsNVIkZMRBQEaOBDrl1mE/KjC3r3bVSFO24yABffsCJ1XXwI9MzO55nybps9RuC7Y7dADGnUQ672sdfqfJbmjVxJNO0l1JIK52Z2QAPx9JJj1ey5igSQK++474RuXlOjleSts//mi60AQAXHs5Gahfwy8gnT866u/19bLipVGPgKBjR6I2SpI8CJixfQ15v/0ffobGc7RjsZ3q6wI24Mwz5dLXN9wgKu7poKyMpEatW0fXiZUwdy5w5m/644zVz2FmwW/Q8uKrJMzNhVUDJIlM7p52Gsn16dGD+HUxfVk34oILgCuuIGPktdfKVcAYIKXJT19pHZqRh5ntV6B2f6RkaarYAUAycL1e4oyaWf9EkuSV6kKJsg89RH599VVSSWjFCtJpDUPeKKzm/VDi1bs3/vjnbFRXk7QnmgdJQ97GjDHhEFu1/cMP5O2cnY2bH6hAnz6EP9D8F1rlbfToGAQAkMOETOb8AAiHOE6ddBDZ2WQm5IMPGCg/a0Ix8j17Anl5OOYYOQT7T38iRITm+xiGvAHxkZ9Qf7vyomacdhoht/fdZ7HYASBfoMOHucTguhHZWRL6+sjq6U9sjY4Tra+XH3fNBXsp4iABvowgrvW8DgB4end0BqskhSqyIUbxoTid0euzZgEAph24UjNB+IsvTNiOQwEBgOvz3gQAvNJ8pWaVbEu2d+ywVOf2ukIilX8YvABb90TL8vSan3aawRjauTMZ5JTVVkzgZ4WfIA+N2IC+WLAzOqHoyy9JU447Th6eo5CREVd/O6NsI3piCxpQiBm10dVotm8nt9Hr1Yz+lCHIDz/87W/EC969m4TC+f28z8i16F3dgJm9HsSmnJ/gRryETLRhCc7AtQ3P45h7foE77vRg+fIES2TbjC1biCD1y18SnjBsGJmQcGENBvN45hkiXa1fT8JlGF3wlCY/pz9yMU47xY+2YCYGDSKhnDQM2bTyk5cnxxfQEqBG2L6dOI+ZmeH/O/10ElsaCJDQRqr6XHihQcgbhVVnmJKfk05Cfj4ZCwHijNfUmMz3oYiXeJ14IjLzMsOlr595hjy0MUtcK2G13YcPh29qtwv74fe/J19PmgR8/z35HLPSG4VV5YeSH8X6PrfcQmZH6+pIVUeq/BgWOwDiVn4AwHPKyeHIh+nT5fwqU/k+AAnxpMndQv0h8Hhw5zwS9vD6Rx3CFcYoPvmE+Be9ehmoAEBcKgS+/BK3N/0NXvixcFVZuEIjxbffkmc6N1ezDkNitnfswITtD6AEh7BlXwf83/9F/vngQbli5MiRNttuacH5yx9CH2xAXWtuOOeYIhAA5s4lnw0jUugD39hoqfTyCZ8/hwvwEYLw4pFHov9OnyvDdmdmyg+ehbYXf/g6bgSJFZ4y1RPlB1DbMSNx4rjunv+8iYl4CgDw92nZUcuc0fH79NN1CntQxEG8BGxCXh4wezYZFObNIy9AN3nvbkB9PXEGe/YEJk9Gz6bVeOmUl7DjnW/x17+SOcCDB0kk1imnkLH9nnvIeMeLS27YQBYv7dePjAGZmSTCY8ECkyHtbkZFBfDmm2RWZdYsuV6305CSEHV1dRIAqa6uLua+y5ZJ0jHHSBIZASSpuFiS7rlHkvr1I7+/844JgzfeSHa+++7Y+/7f/5F9Bw2K+HrlSvkcysrI9j//MWH76qvJzo89ZmJnSZIuuyxi/0BAkn76U/LVGWeQbW6uJDU1mTjWn/9M/uHmm83Zvususv9NN4W/Ov988tWpp5Kt1ytJhw+bONasWeQfhg0zZ3vBArL/ccdJkiRJdXWSVFEhX3NAkj7+2NyhpLffJv/Qv7+5/SdMIPtPnhzx9cqVpL3Ufl6eJLW2xjjWjz/KFyoQiG27vl6SPB7yP3v3SpIkSVddFdnu3/3OXDMkSZKkvn3JP336qel/sfI8uglWznvkSHJZxoyRpGBQ/v6CC8j3f/xjjAN8/DHZ8fjjzZ/gDTdIEiD9svd/JUCSBg+WpLY2+c+33UYOefnlMY6zfj3ZMT9fkvx+c7YffliSAOnBbq9IgCR16UK6GsXTT2sOc9FobJSkjAyy85495mz/5z+SBEivlf1OAiSpoECSdu6U/zx3Ljlchw6S1NIS41idO5Odv/zSnO1VqyQJkBb7hkoAebSWL5f//MMP8uO2fXuMYw0bRnZ85RVztg8flqTsbGk7jpWyswJR74imJvndMXdujGPddJPJjhlCMChJPXpIjciVKoubJECSpkyJ/DN9j/z97zGO9dRTZMdx48zZDiEZxxHXnvNbb8kvgPvuixy00hW1tZJ0771k4KDXpmdP8pApro/fL0nvvy9J48cTX0n5Lu3QgYy3zz1Hhgqzw2k8OHhQkl5+WX730J/zz5ekDRucs8sNzzwjN/LZZ+M6hJXnMeXJjyQRh3P6dEnq0yeyEwGStGaNiQO8+CLZecSI2PvefTfZ98Ybo/50+eWRjnBDgwnb991H/uFnPzOxsyRJ3bqR/RcuDH+1ZElkm02/k156ifzDkCHm9h8xguz/z3+Gv1q/PpIAnH22SdsLF5J/6NbN3MD9t7+R/cePD39Fb5uley1JZNShDtuOHbH3HzyY7Pv221F/mjRJtj98uAnbra0WvCtJkhYtksLeaQjbt0tSTo5s99FHTdilOO888k8vv2z6X1zrAMSAlfNet06SfD5yaf76V9Ilv/iC/J6RIUmbN8c4wL598g0xc52OHCFePyDVvvOVVFRE/vU3vyGcePNm+R7H5Kl+f/hYph4Cv59MIgBS43MzpK5dyb9efDHpnocOSVJ1NfnuuediHy480/TeeyZ2lsLjSOBP90innUb+9bTTCPlqa5Od8N/+1sSxxo0jOz/xhDnbt95K9r/ssvAkwnHHSVJNDbnnP/85+W7UKBPHuvNOa5NH06aR/fv2le6/Lxh2ttavJ3+eOpX8uWtXSWpvj3Gsf/7TwqAjyeNtfr702kstEkD6O32NfPgh+XN2NvEhDbF4Mdm5qsqc7RCScRxx9Tk/8YQ85vz+98566m4FHaivuEKSMjPl69GnD3EKY8xGHj0qSW+8QXw3JWeiPwUFxKe5+Wbiuy9YQCZqYj6fKrS2StLatYSHTZpExjil3+TxkPH3q6/ivxRJgfvvlxv94IOWSbsgPzoIBIjSc8op8kSoKQXk229l2h/rZowaRfZ9/vmoP23YIPvUMWdrKZYtM8+WDh6UO45KXqECEkA4jSns2iU74sqpVy0Eg5JUUkL2VU6VSpJ0++2y7UceMWn7yBHypgXI9Y+Fiy6K8vT9fkkaMEC2feSISduSJMtkGvcxAn6/7IVu2RL154YGKew8qoQhfZx5pskpVokofIAkXXJJxNf33CO3+/XXTdqVJFnlNH2yLncADGD1vKlvCpB+RfmExjyHNmhHUExM6OLvfyf79usnScGg9M47su0TTpCkjh3J53POMfl+GDrUvApBjZWWSlJDg7RkiSRlZckTpSFeJPXoIUnNzSZsX3+9PAMdC6tXy4xy2zZp61bZ6ejShShNgCQVFkrS7t0mbD/0EPmHq66Kve+hQ2ScBSRpwQLpwAFJ6t6d/NqxoyzkeDyStHSpCdtvvkn+4eSTY+8bCJCLG5r1bGmRwsSvsPD/27v74CjKOw7g38vbBSgJIykJKYhErSCEt1B8gQhD23SAyjC0VBCQGR0rFVrSMAqWWhw6GArjS5G3Yhla5S11JjCIWg2U18EahCRNQRKUNEFejCAkwZiQ3D7943GzudfsXu6yu3ffz8z+wd3e7cPe7m+fl9/zRIiHH9aeHVu36jj2tyNYIilJ3wjyT38q93/6aaEosq4IyPri9OmirfG9eLGOY9+8qRX28891fECyYxyxfJnVUTi1xV5ba3aJukZFhXyG3XOPe2vl/vtlJ6Wee8JDS4vsSF6+XPbP9Ozp3Rhq31hJS5PxasIEIaZMkf2yM2fK5JypU+V3ZGbK2KJWszy3YcNkCPv009CfIktSFG0AAZCBSFeqkMTGTwcURbag9dSphRCyu1GtiAfq4lUULS/ho4987rJwobzQ9+83UFh1NOettwLvqz7ov039aq+mRj7XExNlL6Zu2dnyO195JfB+W7dqT0uPXJRr1+RpiYnRejF1+fnP5Xfm5QXeb9Mm7WbxOO8ffCBfTkszcFwhZM4HIKNWIGfPao1TPwG1rEw2AK9d03nsDRvkd44aFXi/khKtVuLRUKqvl/9nQLafdfs23Ulf17Zk+QqAH0bLrShCvPSSNgIEyDZFQ4POA6opqatWBd6voUGI1FS571//2vbym2+6j+gNHqw/k6xtFGL+/MD7uVxCDB8u922X5vvPf7r3fKamyutaF/V61nNNqeeo3Uh3cbE20gTIVJR9+3Qe+/33tZZaR9Qeg2HD2lqU5865Zww4HDLlT5fz57WY2FEr8c03tQ62by+o2lqtD0bdfvlLnY3dlhYtZ6ejHBm1g83hkJVGIbMVp093P/b48To7C4XQep1279b5AXvGEVuUuaBAa9SnpMiU8khLg1MUWblYvVrLxFC3Hj3kjaO7wqdPa6uMgW+8IcTSpbL/9e673UdsjGxJSXJ6wJNPynCgJ/EjYm3cqJ3IAQOEePttXR9j4ycc1EkrgbrRy8vlPnFxfh92LpcQV68aPPYzz2itYH9eeUW7i/w8nU+fDuL+V7u7H3jA/z47d2o9fc8+63OXykr9afdt9uyR39m3r/8h+7fe0rpN/PQsv/uuwQaAEDKqqTUtf0/869e1xqHe1EA9vvxSq2H7q7hUVmqTmrKzfZaxokJOQTPks8+037KkRNdHbFEB8CHYcl+8KC/5/fsNZpKovbAdXStqJfzOO73SMmprhdi2Td4aukZdVLt3a8MngXo9t2zRnsQeger6dflQ3r5dDpLopt5LiYmBU/7UFM6YGK/0vJs3ZZ3t9df1ZaK6FVp9iJ4963+/qiqtseBRYW9qkvfR2rVyQEU3RZGxCwjcWmtoEOL22+V++flub7W2yuf+mjVCFBUZrLOqo32BRpBdLi2GzZnjVfwDB2Q/0M6d7vPNOjR/vvzOJ57Q/RE7xhHblLmszD0NIitL9mjYuRF044a8rxYs0DqI1S02Vk6O+fvf9aUZh1Brq0wNPXVKiHfeEWLXLhlW166Vj4D162VK/htvyL6Z0lK5v51/irD48EMtzQCQE2xragJ+hI2fcFBnF0+aJLvFPB0/rlVGx48P7bGLi7UeDF8V8XXrtAvkD38I7bEvXdIaF75qHYWFWuXiySeDGk72q7lZpt4A8snv6cABLR/nqadCGz0URVsp4513vN+/ckXrIU9Kkr9/KE2ZIr/7+ee93/v8cy2FasQIg/l8OqiTHWbO1LW7bSoAHrq83BcvavdSVZXvfT78ULufdK3GotM332ijhIcO+d6nqkquCAPoX2BFD0XRhk/+9jff+9y4IRt7ahwJpUD3khBylERtKIwfH9o4kpvb8b305JNaD6evZ0uw1BG3QKtSqJ1m3buHtqv54EH5vcnJOlalkOwYR2xV5uZmIVas0PJ1Abmoz5//bCAtwSSKIkdS//EPmUYxYoTWSaduCQlydHn9+uhJ74t09fWyQz0+XsaSbxd18oeNn3A4dEiruNx7r8xNV+3cqaXFjRjR8fwYoxRFq+x6TqpXJ7YCcuw1HN0HDz3kuwdx3z5tEuHcuaFt+KjUVYvmzXN//eOPtSD+s5+FZzKneuwZM9y72auqtPz81FTdIySGbN8uvz8jw/03vXZNW5Htrrt0zD4OgjpfICbG5zwmT7aqALRjSrknTtQa654qKrRcxUCjvMF6/HH53T/5iXecqK2VFSFAjnIbnbHbkT/+UatseQ4hNDZqSxr17x/6xvzOnfK7v/td7/xxl0trfHTvrut6N+Tjj7WKma+U6faT0nXnQut09aoWn311Hu3bpzW0g1xdyS+XS+s8eu01XR+xYxyxY5lFba1MJW+fQxsbKyenrFkjJ7TpbLCGnKLI8h0/LucnLlok6x9qp4znduedcpRx716dK0iRLZ09q2vBHDZ+wmX/fq1ykpgo8xLVhzogZ6XqngBgkJqzn5golxw6cEAGB7VBlpcXvnFTdWQpPl4uObJnj8wlUxt8jzwS+sqS6uhR7dgzZshGwYkT2ozviRPDF6jVCUOAnHSwdq2szKgTEO64I/SVJdXNm1qe9rhxsnfu7Fkt/TI93f/oQSioazj7qqR7sGUFQJhU7iNH5Hl1OOQoiKLIiuKuXdp8wcxM93WlQ6WiQhspzc+Xx1Vzm9T0grS00HfeCKFN/ANkmoraACor01ag6dbNa7GUkGhulhOkAJmYr46uVFdrq8E5HD5Xa+w0RdHW+x8zRqa0CiHzBtVsAsB9XelQUlebueMOmdIqhOzIWbVKS62dMyc8zw515Ck5WddkTzvGETuWuc1XX8mGqZrB0H6Lj5f35RNPyFjx1lvyuVtdHfzoZFOT7LmvqJCdyTt2yEmUixfLOkRWlv9GjtqBMGqUvG8KCnSueELRhI2fcPriC5n65nlj5uWFdynJS5fkje8rKCxcGN6E0fp6rcfac5s2zWAyuEEul1Z58NxGjQp/Pu+WLbI32vPYQ4aEP/iqiy54brfdJtfFDCe1kp6Q0OGsertWAEwr99NPa79l//5aowCQFYAOhvY7RV0SHpDzUdqvJHDHHYHnxXSWOgKjjsKoaW6AnOh/5Ej4jn30qNbw69VL3r9q2kxCgpxIFS7nzmmrRfToISubascRENSSrrrduKE1bNXKY/uVK2bPDl/8bm2V89sAXX+qwY5xxI5l9unTT2UH2+TJ7vHI39atmxzZu+sueS9lZcl5wWPGyGts2DCZoXDXXXI6QPvrXc/Wv79cyjI3V3YSlZWFt55BEYGNn3BzuWSPRXy8HC7+y1+65riKIuf/PPWUlr8f6rkugZSXy0Xo1ZWoJk/umuFx9f/93HPa3IHvfz+8lcT2mprkKJ/aCLr//q7Lka6ulnn56rJP3/mOznV2Q2D2bPn/7mBmven3Y5BMK7fLJf/YXvsKQa9ecg3VcN9PiiJn3bZfp9XplA0yA0uKBm3HDm3UFpANkBkzOpzIGhL792vpw+o2caLBVQyC5DnhHJAVxPffD/+xL1zQUpfVrV8/mTkQ7mfHl1/KkT4dKUl2jCN2LHOHFEWOEu7cKZeLnjNHPvPS07UOhM5sSUlyCeoJE+Tf4Fi8WNan9uyRnXq6lxQkcmfkfnQIIQRspr6+HsnJyairq0NSUpJ5BTl/HmhpAe65p+uP3dgInDsHDBsGOBxde+zWVuC//wUyM4HY2K49NgBUVwMpKUCPHl173OZm4KOPgPvuA5zOrj02AHzxhfyt+/Tp+mMHYJn70SDTy33jhryPEhPlfZyQ0HXHvnkTKCuT19Pw4V17L33zDVBaKu+nzEygd++uO3ZLC1BSIs/94MFA//5dd2xFkef88mUgI0M+N7oqdgsBfPIJ8NlnQN++wMiR5sTuAEy/H4NgxzJ3ihAydly9Cnz1FdDUJO9jdYuL896SkoDkZLn17Gm5644ih5H70dTGz4YNG7BmzRpcvnwZQ4YMwauvvors7OwOPxd1AYfIwsy8H4ONIQDjCJGV2DGOMIYQWYeR+zGmi8rkpaCgALm5uVi2bBlKSkqQnZ2NSZMmoaamxqwiEZGNMIYQUWcxjhBFH9NGfu677z6MGjUKGzdubHtt8ODBmDZtGvLz8wN+lr0tRNZh1v3YmRgCMI4QWYkd4whjCJF1WH7k59atWzh58iRycnLcXs/JycHx48e99m9ubkZ9fb3bRkTRy2gMARhHiMgd6yJE0cmUxs/Vq1fhcrmQmprq9npqaiquXLnitX9+fj6Sk5Pbtv5dOUmViCzHaAwBGEeIyB3rIkTRybQ5PwDg8FjpRgjh9RoAPPfcc6irq2vbLly40FVFJCIL0xtDAMYRIvKNdRGi6BJnxkFTUlIQGxvr1bNSW1vr1QMDAE6nE04zlhYmIksyGkMAxhEicse6CFF0MmXkJyEhAVlZWSgqKnJ7vaioCA8++KAZRSIiG2EMIaLOYhwhik6mjPwAQF5eHubOnYvRo0fjgQcewObNm1FTU4P58+ebVSQishHGECLqLMYRouhjWuPnkUcewbVr17BixQpcvnwZQ4cOxbvvvosBAwaYVSQishHGECLqLMYRouhj2t/56QyurU9kHXa9H+1abqJIZMf70Y5lJopURu5H00Z+OkNtr3GNfSLzqfeh3fpRGEeIrMOOcYQxhMg6jMQQWzZ+GhoaAIBr7BNZSENDA5KTk80uhm6MI0TWY6c4whhCZD16Yogt094URcGlS5fQs2dPv3/TQ1VfX4/+/fvjwoULHJbuJJ7L0Ii08yiEQENDA9LT0xETY+qfDjNEbxyJtN/LTDyXoRNp59KOccRIXSSc7HotsNxdK9LLbSSG2HLkJyYmBv369TP0maSkJFv92FbGcxkakXQe7dJT257ROBJJv5fZeC5DJ5LOpd3iSDB1kXCy67XAcnetSC633hhij+4VIiIiIiKiTmLjh4iIiIiIokLEN36cTieWL18Op9NpdlFsj+cyNHge7YW/V+jwXIYOzyWp7HotsNxdi+XW2HLBAyIiIiIiIqMifuSHiIiIiIgIYOOHiIiIiIiiBBs/REREREQUFdj4ISIiIiKiqBDRjZ8NGzZg4MCBSExMRFZWFo4ePWp2kSwvPz8fP/jBD9CzZ0/06dMH06ZNQ0VFhds+Qgi88MILSE9PR7du3TBhwgScPn3apBLbQ35+PhwOB3Jzc9te43m0B8YRYxhDwodxhPzxdW1Y1cWLFzFnzhz07t0b3bt3x4gRI3Dy5EmzixVQa2srfv/732PgwIHo1q0bMjIysGLFCiiKYnbRvBw5cgQPP/ww0tPT4XA4sGfPHrf3rRozApW7paUFS5YsQWZmJnr06IH09HQ89thjuHTpUlDHitjGT0FBAXJzc7Fs2TKUlJQgOzsbkyZNQk1NjdlFs7TDhw9jwYIF+Pe//42ioiK0trYiJycHX3/9dds+q1evxssvv4x169bhxIkTSEtLw49//GM0NDSYWHLrOnHiBDZv3oxhw4a5vc7zaH2MI8YxhoQH4wj54+/asKLr169j7NixiI+Px3vvvYczZ87gpZdeQq9evcwuWkB/+tOfsGnTJqxbtw6ffPIJVq9ejTVr1uC1114zu2hevv76awwfPhzr1q3z+b5VY0agcjc2NuLUqVN4/vnncerUKRQWFqKyshJTp04N7mAiQo0ZM0bMnz/f7bVBgwaJpUuXmlQie6qtrRUAxOHDh4UQQiiKItLS0sSqVava9mlqahLJycli06ZNZhXTshoaGsTdd98tioqKxPjx48WiRYuEEDyPdsE40nmMIZ3HOEL++Ls2rGrJkiVi3LhxZhfDsClTpojHH3/c7bXp06eLOXPmmFQifQCI3bt3t/3bLjHDs9y+FBcXCwCiurra8PdH5MjPrVu3cPLkSeTk5Li9npOTg+PHj5tUKnuqq6sDANx2220AgKqqKly5csXt3DqdTowfP57n1ocFCxZgypQp+NGPfuT2Os+j9TGOhAZjSOcxjpA//q4Nq9q7dy9Gjx6NGTNmoE+fPhg5ciRef/11s4vVoXHjxuHAgQOorKwEAJSVleHYsWOYPHmyySUzJpJiRl1dHRwOR1CjhnGhL475rl69CpfLhdTUVLfXU1NTceXKFZNKZT9CCOTl5WHcuHEYOnQoALSdP1/ntrq6usvLaGW7du3CqVOncOLECa/3eB6tj3Gk8xhDOo9xhPwJdG1Y1fnz57Fx40bk5eXhd7/7HYqLi/Gb3/wGTqcTjz32mNnF82vJkiWoq6vDoEGDEBsbC5fLhZUrV2LWrFlmF82QSIkZTU1NWLp0KR599FEkJSUZ/nxENn5UDofD7d9CCK/XyL+FCxfiP//5D44dO+b1Hs9tYBcuXMCiRYvwwQcfIDEx0e9+PI/Wx98oeIwhncM4Qv7ovTasRlEUjB49Gi+++CIAYOTIkTh9+jQ2btxo6cZPQUEBtm3bhh07dmDIkCEoLS1Fbm4u0tPTMW/ePLOLZ5idY0ZLSwtmzpwJRVGwYcOGoL4jItPeUlJSEBsb69U7W1tb69XaJd9+/etfY+/evTh48CD69evX9npaWhoA8Nx24OTJk6itrUVWVhbi4uIQFxeHw4cPY+3atYiLi2s7VzyP1sU40jmMIZ3HOEL+dHRtuFwus4voU9++fXHvvfe6vTZ48GDLLyLzzDPPYOnSpZg5cyYyMzMxd+5c/Pa3v0V+fr7ZRTPE7vG3paUFv/jFL1BVVYWioqKgRn2ACG38JCQkICsrC0VFRW6vFxUV4cEHHzSpVPYghMDChQtRWFiIf/3rXxg4cKDb+wMHDkRaWprbub116xYOHz7Mc9vOD3/4Q5SXl6O0tLRtGz16NGbPno3S0lJkZGTwPFoc40hwGENCh3GE/Ono2oiNjTW7iD6NHTvWa+n7yspKDBgwwKQS6dPY2IiYGPcqc2xsrCWXug7EzvFXbficO3cO+/fvR+/evYP/MsNLJNjErl27RHx8vNiyZYs4c+aMyM3NFT169BD/+9//zC6apf3qV78SycnJ4tChQ+Ly5cttW2NjY9s+q1atEsnJyaKwsFCUl5eLWbNmib59+4r6+noTS259nivx8DxaH+OIcYwh4cU4Qv7YYbW34uJiERcXJ1auXCnOnTsntm/fLrp37y62bdtmdtECmjdvnvje974n9u3bJ6qqqkRhYaFISUkRzz77rNlF89LQ0CBKSkpESUmJACBefvllUVJS0rYqmlVjRqByt7S0iKlTp4p+/fqJ0tJSt2dLc3Oz4WNFbONHCCHWr18vBgwYIBISEsSoUaPallol/wD43LZu3dq2j6IoYvny5SItLU04nU7x0EMPifLycvMKbROeDyaeR3tgHDGGMSS8GEfIHzs0foQQ4u233xZDhw4VTqdTDBo0SGzevNnsInWovr5eLFq0SNx+++0iMTFRZGRkiGXLlgVV8Q63gwcP+ozB8+bNE0JYN2YEKndVVZXfZ8vBgwcNH8shhBDBjxsRERERERHZQ0TO+SEiIiIiIvLExg8REREREUUFNn6IiIiIiCgqsPFDRERERERRgY0fIiIiIiKKCmz8EBERERFRVGDjh4iIiIiIogIbP0REREREFBXY+CEiIiIioqjAxg8REREREUUFNn6IiIiIiCgqsPFDRERERERR4f+V/iBbCDIYLAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def cy_diffeq(dy, t, y):\n",
     "    dy[0] = (1. - 0.01 * y[1]) * y[0]\n",
@@ -598,49 +519,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "094818de",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Was Integration was successful? True\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "solve_ivp shape: (2, 173)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Was Integration was successful? True\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "pysolve_ivp shape: (2, 173)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def diffeq(t, y):\n",
     "    dy = np.empty_like(y)\n",
@@ -701,56 +583,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "18eb011a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Was Integration was successful? True\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "solve_ivp shape: (2, 175)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Was Integration was successful? True\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "pysolve_ivp shape: (2, 175)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def diffeq(t, y):\n",
     "    dy = np.empty_like(y)\n",
@@ -821,7 +657,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/Demos/2 - Advanced CySolver Examples.ipynb b/Demos/2 - Advanced CySolver Examples.ipynb
index b8b7685..5b0bf9d 100644
--- a/Demos/2 - Advanced CySolver Examples.ipynb	
+++ b/Demos/2 - Advanced CySolver Examples.ipynb	
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "350bd407-9563-4fa4-983e-02046bdcc051",
    "metadata": {},
    "outputs": [
@@ -39,7 +39,7 @@
      "output_type": "stream",
      "text": [
       "Content of stdout:\n",
-      "_cython_magic_87b7c72453f4a8f721d8a8ee117020eaa2e4e253.cpp\n",
+      "_cython_magic_755db41242bd248520a9ea765b917df24f104a5c.cpp\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\dense.cpp(155): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(76): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
@@ -48,29 +48,29 @@
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(286): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(323): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(425): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(239): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(318): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(326): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(332): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(344): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(550): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(237): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(316): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(324): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(330): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(342): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(548): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(54): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(56): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(61): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(80): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(91): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(173): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(239): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(457): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(523): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(587): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(592): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(616): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(634): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(1000): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(228): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_87b7c72453f4a8f721d8a8ee117020eaa2e4e253.cpp(25134): warning C4551: function call missing argument list\n",
-      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_87b7c72453f4a8f721d8a8ee117020eaa2e4e253.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_87b7c72453f4a8f721d8a8ee117020eaa2e4e253.cp311-win_amd64.exp\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(164): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(230): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(448): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(514): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(578): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(583): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(607): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(625): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(991): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(230): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_755db41242bd248520a9ea765b917df24f104a5c.cpp(25134): warning C4551: function call missing argument list\n",
+      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_755db41242bd248520a9ea765b917df24f104a5c.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_755db41242bd248520a9ea765b917df24f104a5c.cp311-win_amd64.exp\n",
       "Generating code\n",
       "Finished generating code\n",
       "\n",
@@ -93,8 +93,11 @@
     "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc\n",
     "\n",
     "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
-    "    # Arguments are passed in as a void pointer to preallocated and instantiated memory. \n",
-    "    # This memory could be stack or heap allocated. If it is not valid then you will run into seg faults.\n",
+    "    # Arguments are passed in as char pointers.\n",
+    "    # These point to preallocated and instantiated memory in the CySolver class that is filled with user-provided data.\n",
+    "\n",
+    "    # The user can then recast the generic char pointers back to original format of args_ptr. In this case, an array of doubles. \n",
+    "    # Seg faults will occur if this function recasts to the incorrect type.\n",
     "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
     "    cdef double l = args_dbl_ptr[0]\n",
     "    cdef double m = args_dbl_ptr[1]\n",
@@ -134,7 +137,7 @@
     "    # We need to tell CySolver the size of the additional argument structure so that it can make a copy.\n",
     "    cdef size_t size_of_args = sizeof(double) * 3\n",
     "\n",
-    "    # To work with cysolve_ivp, we must cast the args ptr to a void pointer\n",
+    "    # To work with cysolve_ivp, we must cast the args ptr to a char pointer\n",
     "    cdef char* args_ptr = <char*>args_dbl_ptr\n",
     "\n",
     "    cdef CySolveOutput result = cysolve_ivp(\n",
@@ -162,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "c87d9c2d-a280-4a5f-bcf1-ed432dc42368",
    "metadata": {},
    "outputs": [
@@ -198,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "e57015f3-0ccf-4a02-becc-60027ca7741f",
    "metadata": {},
    "outputs": [
@@ -207,7 +210,7 @@
      "output_type": "stream",
      "text": [
       "Content of stdout:\n",
-      "_cython_magic_ee620386e270820905f6a4bc1cdccf7a8208b729.cpp\n",
+      "_cython_magic_37b1c1169d625033a90ed1442266158e6b00d6a5.cpp\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\dense.cpp(155): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(76): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
@@ -216,29 +219,29 @@
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(286): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(323): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(425): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(239): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(318): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(326): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(332): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(344): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(550): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(237): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(316): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(324): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(330): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(342): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(548): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(54): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(56): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(61): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(80): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(91): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(173): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(239): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(457): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(523): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(587): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(592): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(616): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(634): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(1000): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(228): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_ee620386e270820905f6a4bc1cdccf7a8208b729.cpp(25205): warning C4551: function call missing argument list\n",
-      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_ee620386e270820905f6a4bc1cdccf7a8208b729.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_ee620386e270820905f6a4bc1cdccf7a8208b729.cp311-win_amd64.exp\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(164): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(230): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(448): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(514): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(578): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(583): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(607): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(625): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(991): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(230): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_37b1c1169d625033a90ed1442266158e6b00d6a5.cpp(25205): warning C4551: function call missing argument list\n",
+      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_37b1c1169d625033a90ed1442266158e6b00d6a5.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_37b1c1169d625033a90ed1442266158e6b00d6a5.cp311-win_amd64.exp\n",
       "Generating code\n",
       "Finished generating code\n",
       "\n",
@@ -269,8 +272,8 @@
     "    double m\n",
     "\n",
     "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
-    "    # Arg pointer still must be listed as a void pointer or it will not work with cysolve_ivp.\n",
-    "    # But now the user can recast that void pointer to the structure they wish.\n",
+    "    # Arg pointer still must be listed as a char pointer or it will not work with cysolve_ivp.\n",
+    "    # But now the user can recast that char pointer to the structure they wish.\n",
     "    cdef PendulumArgs* pendulum_args_ptr = <PendulumArgs*>args_ptr\n",
     "    # And easily access its members which can be many heterogeneous types.\n",
     "    cdef double l = pendulum_args_ptr.l\n",
@@ -311,7 +314,7 @@
     "    # We now have a a structure that we need to allocate memory for.\n",
     "    # For this example, let's do it on the stack. \n",
     "    cdef PendulumArgs pendulum_args = PendulumArgs(True, 9.81, 1.0, 1.0)\n",
-    "    # We need to pass in a void pointer to cysolve_ivp, so let's cast the address of the struct to char*\n",
+    "    # We need to pass in a char pointer to cysolve_ivp, so let's cast the address of the struct to char*\n",
     "    cdef char* args_ptr = <char*>&pendulum_args\n",
     "\n",
     "    # It is important that you take the size of the underlying structure and _not_ the `args_ptr` which would just be the size of the pointer PendulumArgs*\n",
@@ -342,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "4044656e-c299-40e1-b54f-ce6f01931a38",
    "metadata": {},
    "outputs": [
@@ -378,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "13202bf2-766d-4d59-977c-c0e9a96fae87",
    "metadata": {},
    "outputs": [
@@ -387,7 +390,7 @@
      "output_type": "stream",
      "text": [
       "Content of stdout:\n",
-      "_cython_magic_628f2634c26bc0e6fb4c2ec30ec99ca5eadbc234.cpp\n",
+      "_cython_magic_47084ca4d8cf6fb997ab97996accaabb177e4e69.cpp\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\dense.cpp(155): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(76): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
@@ -396,29 +399,29 @@
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(286): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(323): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(425): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(239): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(318): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(326): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(332): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(344): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(550): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(237): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(316): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(324): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(330): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(342): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(548): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(54): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(56): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(61): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(80): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
       "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(91): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(173): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(239): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(457): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(523): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(587): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(592): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(616): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(634): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(1000): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(228): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_628f2634c26bc0e6fb4c2ec30ec99ca5eadbc234.cpp(25364): warning C4551: function call missing argument list\n",
-      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_628f2634c26bc0e6fb4c2ec30ec99ca5eadbc234.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_628f2634c26bc0e6fb4c2ec30ec99ca5eadbc234.cp311-win_amd64.exp\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(164): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(230): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(448): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(514): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(578): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(583): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(607): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(625): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(991): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(230): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
+      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_47084ca4d8cf6fb997ab97996accaabb177e4e69.cpp(25364): warning C4551: function call missing argument list\n",
+      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_47084ca4d8cf6fb997ab97996accaabb177e4e69.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_47084ca4d8cf6fb997ab97996accaabb177e4e69.cp311-win_amd64.exp\n",
       "Generating code\n",
       "Finished generating code\n",
       "\n",
@@ -438,9 +441,9 @@
     "\n",
     "from libc.math cimport sin\n",
     "\n",
-    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType,MAX_STEP, CySolveOutput, PreEvalFunc\n",
+    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc\n",
     "\n",
-    "cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, const char* args_ptr) noexcept nogil:\n",
+    "cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
     "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
     "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
     "\n",
@@ -452,7 +455,7 @@
     "    output_dbl_ptr[0] = torque\n",
     "    output_dbl_ptr[1] = 0.0  # No Drag\n",
     "\n",
-    "cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, const char* args_ptr) noexcept nogil:\n",
+    "cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
     "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
     "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
     "\n",
@@ -480,12 +483,12 @@
     "    cdef double[4] pre_eval_storage\n",
     "    cdef double* pre_eval_storage_ptr = &pre_eval_storage[0]\n",
     "\n",
-    "    # Cast storage to void so we can call function\n",
-    "    cdef char* pre_eval_storage_void_ptr = <char*>pre_eval_storage_ptr\n",
+    "    # Cast storage to char so we can call function\n",
+    "    cdef char* pre_eval_storage_char_ptr = <char*>pre_eval_storage_ptr\n",
     "\n",
     "    # Call Pre-Eval Function\n",
     "    # Note that even though CyRK calls this function a \"pre-eval\" function, it can be placed anywhere inside the diffeq function. \n",
-    "    pre_eval_func(pre_eval_storage_void_ptr, t, y_ptr, args_ptr)\n",
+    "    pre_eval_func(pre_eval_storage_char_ptr, t, y_ptr, args_ptr)\n",
     "\n",
     "    cdef double y0 = y_ptr[0]\n",
     "    cdef double y1 = y_ptr[1]\n",
@@ -557,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "ced2203e-13c2-41b1-83f9-b8e3d5e2d751",
    "metadata": {},
    "outputs": [
diff --git a/Documentation/Advanced CySolver.md b/Documentation/Advanced CySolver.md
index 2c0c936..5d8cc9a 100644
--- a/Documentation/Advanced CySolver.md	
+++ b/Documentation/Advanced CySolver.md	
@@ -25,8 +25,11 @@ from libc.math cimport sin
 from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc
 
 cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:
-    # Arguments are passed in as a void pointer to preallocated and instantiated memory. 
-    # This memory could be stack or heap allocated. If it is not valid then you will run into seg faults.
+    # Arguments are passed in as char pointers.
+    # These point to preallocated and instantiated memory in the CySolver class that is filled with user-provided data.
+
+    # The user can then recast the generic char pointers back to original format of args_ptr. In this case, an array of doubles. 
+    # Seg faults will occur if this function recasts to the incorrect type.
     cdef double* args_dbl_ptr = <double*>args_ptr
     cdef double l = args_dbl_ptr[0]
     cdef double m = args_dbl_ptr[1]
@@ -115,8 +118,8 @@ cdef struct PendulumArgs:
     double m
 
 cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:
-    # Arg pointer still must be listed as a void pointer or it will not work with cysolve_ivp.
-    # But now the user can recast that void pointer to the structure they wish.
+    # Arg pointer still must be listed as a char pointer or it will not work with cysolve_ivp.
+    # But now the user can recast that char pointer to the structure they wish.
     cdef PendulumArgs* pendulum_args_ptr = <PendulumArgs*>args_ptr
     # And easily access its members which can be many heterogeneous types.
     cdef double l = pendulum_args_ptr.l
@@ -204,7 +207,7 @@ from libc.math cimport sin
 
 from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType,MAX_STEP, CySolveOutput, PreEvalFunc
 
-cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:
+cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, const char* args_ptr) noexcept nogil:
     # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)
     cdef double* args_dbl_ptr = <double*>args_ptr
 
@@ -216,7 +219,7 @@ cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr,
     output_dbl_ptr[0] = torque
     output_dbl_ptr[1] = 0.0  # No Drag
 
-cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:
+cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, const char* args_ptr) noexcept nogil:
     # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)
     cdef double* args_dbl_ptr = <double*>args_ptr
 
@@ -245,11 +248,11 @@ cdef void pendulum_preeval_diffeq(double* dy_ptr, double t, double* y_ptr, char*
     cdef double* pre_eval_storage_ptr = &pre_eval_storage[0]
 
     # Cast storage to char so we can call function
-    cdef char* pre_eval_storage_void_ptr = <char*>pre_eval_storage_ptr
+    cdef char* pre_eval_storage_char_ptr = <char*>pre_eval_storage_ptr
 
     # Call Pre-Eval Function
     # Note that even though CyRK calls this function a "pre-eval" function, it can be placed anywhere inside the diffeq function. 
-    pre_eval_func(pre_eval_storage_void_ptr, t, y_ptr, args_ptr)
+    pre_eval_func(pre_eval_storage_char_ptr, t, y_ptr, args_ptr)
 
     cdef double y0 = y_ptr[0]
     cdef double y1 = y_ptr[1]
diff --git a/Performance/cyrk_performance-DOP853.csv b/Performance/cyrk_performance-DOP853.csv
index 54fb50b..1f2a12c 100644
--- a/Performance/cyrk_performance-DOP853.csv
+++ b/Performance/cyrk_performance-DOP853.csv
@@ -26,3 +26,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.2, 13/11/2024 09:47:14, 1.0383, 0.0482, 0.0459, 0.0011, 0.1791, 0.0065, 10.4672, 0.3348, 0.4292, 0.0054, 1.5538, 0.0164, 0.4595, 0.0036, 0.0361, 0.0011, 0.1058, 0.0015, 4.6616, 0.1080, 0.3028, 0.0064, 0.8637, 0.0168, 1.4801, 0.0422, 0.0815, 0.0003, 0.2740, 0.0060, 20.1617, 0.3500, 1.0886, 0.0310, 3.4030, 0.0480, 1.7564, 0.0416, 0.0840, 0.0008, 0.5297, 0.0179, 23.9814, 0.4823, 1.1206, 0.0153, 6.7479, 0.0770
 0.11.3, 18/11/2024 22:01:05, 1.0395, 0.0643, 0.0446, 0.0007, 0.1775, 0.0072, 10.4019, 0.4997, 0.4034, 0.0051, 1.5558, 0.0211, 0.4460, 0.0060, 0.0335, 0.0003, 0.1022, 0.0008, 4.4008, 0.0505, 0.2929, 0.0032, 0.8257, 0.0232, 1.4250, 0.0155, 0.0852, 0.0005, 0.2547, 0.0017, 20.6687, 0.6711, 1.1036, 0.0036, 3.4398, 0.3229, 1.6677, 0.0198, 0.0893, 0.0052, 0.5148, 0.0099, 25.7176, 2.5321, 1.1436, 0.0118, 6.8288, 0.1691
 0.11.4, 25/11/2024 13:48:29, 0.9518, 0.0095, 0.0469, 0.0005, 0.1849, 0.0158, 9.7986, 0.2271, 0.4914, 0.0471, 1.5895, 0.0413, 0.4717, 0.0174, 0.0371, 0.0021, 0.1015, 0.0013, 4.3503, 0.0397, 0.3076, 0.0072, 0.8205, 0.0108, 1.4690, 0.0353, 0.0912, 0.0009, 0.2552, 0.0035, 19.6132, 0.1880, 1.2057, 0.0062, 3.2306, 0.0464, 1.6630, 0.0246, 0.0927, 0.0004, 0.4967, 0.0064, 22.7714, 0.2059, 1.2428, 0.0109, 6.6800, 0.1388
+0.12.0, 02/12/2024 12:17:22, 0.9420, 0.0109, 0.0171, 0.0003, 0.1698, 0.0039, 9.6794, 0.1666, 0.1034, 0.0018, 1.5133, 0.0398, 0.4358, 0.0067, 0.1285, 0.0066, 0.1006, 0.0013, 4.3306, 0.0946, 0.1849, 0.0048, 0.7820, 0.0092, 1.4031, 0.0022, 0.0288, 0.0003, 0.2469, 0.0040, 19.3999, 0.2563, 0.1347, 0.0012, 3.1234, 0.0767, 1.6662, 0.0114, 0.0296, 0.0003, 0.4822, 0.0092, 23.1414, 0.2182, 0.1386, 0.0009, 6.7406, 0.2205
diff --git a/Performance/cyrk_performance-RK23.csv b/Performance/cyrk_performance-RK23.csv
index cf9f73e..1a843c7 100644
--- a/Performance/cyrk_performance-RK23.csv
+++ b/Performance/cyrk_performance-RK23.csv
@@ -26,3 +26,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.2, 13/11/2024 09:44:47, 58.5798, 92.5131, 0.3027, 0.0073, 0.9652, 0.0611, 50.2554, 3.1065, 3.0509, 0.1734, 11.0730, 0.5532, 3.0001, 0.0297, 0.2962, 0.0300, 0.6303, 0.0107, 29.3821, 0.6157, 2.4788, 0.0442, 8.6115, 2.1469, 54.1616, 85.1339, 0.3808, 0.0043, 1.1505, 0.0435, 83.2819, 0.6580, 6.1863, 0.0625, 22.4879, 0.1939, 59.9757, 93.5368, 0.3924, 0.0084, 1633.8436, 2826.6269, 105.4013, 5.7465, 7.0494, 0.0526, 41.1053, 0.3990
 0.11.3, 18/11/2024 21:58:42, 53.8381, 85.1332, 0.2844, 0.0053, 0.8357, 0.0040, 44.6139, 0.5691, 2.7175, 0.0170, 9.6441, 0.2775, 2.8966, 0.0745, 0.2544, 0.0105, 0.6118, 0.0154, 27.6303, 0.0867, 2.4112, 0.0748, 6.5003, 0.2024, 4.8056, 0.0730, 0.3556, 0.0063, 0.9397, 0.0079, 78.8993, 0.7616, 5.7215, 0.0496, 20.2977, 0.1102, 5.7503, 0.0931, 0.3808, 0.0202, 1430.3917, 2474.4326, 96.3635, 0.9127, 6.3773, 0.0910, 39.3835, 1.9905
 0.11.4, 25/11/2024 13:46:04, 53.6135, 84.9470, 0.2950, 0.0027, 0.8886, 0.0461, 47.7257, 2.7085, 3.0456, 0.1566, 10.3188, 0.4075, 2.8431, 0.0316, 0.2536, 0.0007, 0.6429, 0.0073, 29.1347, 0.7174, 2.5346, 0.0408, 6.6766, 0.1161, 5.1020, 0.1334, 0.3688, 0.0044, 0.9965, 0.0316, 84.4449, 2.4298, 6.4677, 0.2837, 23.8316, 1.0627, 55.1419, 85.4437, 0.3982, 0.0071, 1543.6904, 2670.4149, 101.9026, 1.5866, 6.8547, 0.1089, 36.6832, 0.7182
+0.12.0, 02/12/2024 12:14:56, 54.4094, 86.3513, 0.1211, 0.0003, 0.8848, 0.0332, 45.3977, 0.5062, 1.1182, 0.0135, 9.2274, 0.2593, 2.9169, 0.1081, 1.5773, 0.1054, 0.6041, 0.0131, 30.6447, 3.1379, 2.0259, 0.0780, 6.4170, 0.3496, 69.9247, 112.6772, 0.0592, 0.0013, 1.0330, 0.0465, 81.0514, 1.0440, 0.1393, 0.0016, 21.6085, 1.2794, 5.6090, 0.1235, 0.0585, 0.0006, 1417.2262, 2451.6125, 95.5275, 1.3695, 0.1481, 0.0004, 37.1712, 0.6557
diff --git a/Performance/cyrk_performance-RK45.csv b/Performance/cyrk_performance-RK45.csv
index 445a276..daf22ad 100644
--- a/Performance/cyrk_performance-RK45.csv
+++ b/Performance/cyrk_performance-RK45.csv
@@ -26,3 +26,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.2, 13/11/2024 09:46:20, 1.2497, 0.0190, 0.0645, 0.0006, 0.2333, 0.0074, 12.5791, 0.2532, 0.6023, 0.0184, 2.0329, 0.0377, 0.8824, 0.0160, 0.0676, 0.0001, 0.1932, 0.0074, 8.4859, 0.0106, 0.6328, 0.0072, 1.5491, 0.0287, 1.7246, 0.0188, 0.1056, 0.0016, 0.3201, 0.0090, 25.1528, 0.5846, 1.4855, 0.0071, 4.6009, 0.1266, 2.0235, 0.0174, 0.1135, 0.0025, 0.6057, 0.0057, 29.2690, 0.5148, 1.5547, 0.0225, 8.8452, 0.2573
 0.11.3, 18/11/2024 22:00:10, 1.1977, 0.0041, 0.0614, 0.0006, 0.2097, 0.0059, 11.8970, 0.1254, 0.5723, 0.0119, 1.9069, 0.0544, 0.8432, 0.0066, 0.0659, 0.0009, 0.1745, 0.0030, 8.4187, 0.3196, 0.6140, 0.0064, 1.7302, 0.1918, 1.7725, 0.0502, 0.1088, 0.0022, 0.3057, 0.0097, 24.8389, 1.0028, 1.6105, 0.0791, 4.4626, 0.1301, 2.0433, 0.0796, 0.1261, 0.0023, 0.6580, 0.0422, 29.5806, 1.1944, 1.6120, 0.0302, 9.2682, 0.4399
 0.11.4, 25/11/2024 13:47:34, 1.1965, 0.0067, 0.0656, 0.0007, 0.2081, 0.0011, 12.0259, 0.1258, 0.5992, 0.0031, 1.9689, 0.0350, 0.8615, 0.0301, 0.0710, 0.0015, 0.1781, 0.0037, 8.1765, 0.0930, 0.6150, 0.0051, 1.5196, 0.0210, 1.6505, 0.0132, 0.1175, 0.0006, 0.3060, 0.0093, 24.5149, 0.4404, 1.6367, 0.0394, 4.0881, 0.0509, 1.9450, 0.0223, 0.1238, 0.0014, 0.5835, 0.0187, 28.3889, 0.4499, 1.6710, 0.0108, 8.3209, 0.1614
+0.12.0, 02/12/2024 12:16:25, 1.2212, 0.0493, 0.0318, 0.0002, 0.2298, 0.0241, 12.1300, 0.1803, 0.2564, 0.0027, 1.8721, 0.0454, 0.8283, 0.0036, 0.0742, 0.0015, 0.1715, 0.0021, 8.0965, 0.1355, 0.1188, 0.0037, 1.4860, 0.0148, 1.6563, 0.0227, 0.0348, 0.0005, 0.2871, 0.0072, 23.8156, 0.1253, 0.1607, 0.0018, 3.8900, 0.0687, 1.9042, 0.0077, 0.0353, 0.0007, 0.5619, 0.0085, 29.6907, 0.9592, 0.1699, 0.0039, 8.0727, 0.1076
diff --git a/README.md b/README.md
index bd47cd7..4071123 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 
 ---
 
-<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.11.6 Alpha-orange" alt="CyRK Version 0.11.6 Alpha" /></a>
+<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.12.0 Alpha-orange" alt="CyRK Version 0.12.0 Alpha" /></a>
 
 
 **Runge-Kutta ODE Integrator Implemented in Cython and Numba**
@@ -28,7 +28,7 @@ The [cython-based](https://cython.org/) `cysolver_ivp` function that works with
 An additional benefit of the two cython implementations is that they are pre-compiled. This avoids most of the start-up performance hit experienced by just-in-time compilers like numba.
 
 
-<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png" alt="CyRK Performance Graphic" />
+<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-12-0.png" alt="CyRK Performance Graphic" />
 
 ## Installation
 
diff --git a/Tests/E_CySolver_Tests/test_a_cysolve_ivp.py b/Tests/E_CySolver_Tests/test_a_cysolve_ivp.py
index 78eb29f..f7ee9f9 100644
--- a/Tests/E_CySolver_Tests/test_a_cysolve_ivp.py
+++ b/Tests/E_CySolver_Tests/test_a_cysolve_ivp.py
@@ -187,16 +187,6 @@ def test_cysolve_ivp_all_diffeqs(cysolve_test_func):
 
     result = cytester(cysolve_test_func)
 
-    # There is a weird fail state that occasionally happens on MacOS for Python 3.10 where diffeq 5 (lotkavolterra) fails.
-    # See GitHub Issue [#67](https://github.com/jrenaud90/CyRK/issues/67)
-    import platform
-    on_macos = False
-    if platform.system().lower() == 'darwin':
-        on_macos = True
-    if not result.success and on_macos and cysolve_test_func==5:
-        pytest.skip("Weird macos bug on diffeq5")
-    # on_macos = False
-    
     assert result.success
     assert result.t.size > 0
     assert result.y.size > 0
@@ -208,7 +198,4 @@ def test_cysolve_ivp_all_diffeqs(cysolve_test_func):
 def test_cysolve_extra_output():
     """ Tests if cysolver is able to retain onto additional arguments for later calls when extra-output and dense-output are on """
 
-    print("PRE TEST")
-    print(cy_extra_output_tester())
-    print("POST TEST")
-    # assert cy_extra_output_tester()
+    assert cy_extra_output_tester()
diff --git a/Tests/Untitled.ipynb b/Tests/Untitled.ipynb
deleted file mode 100644
index cb19796..0000000
--- a/Tests/Untitled.ipynb
+++ /dev/null
@@ -1,1051 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3f8b4f01-9b64-4a55-b902-29f4c5e7ec15",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11.6a0.dev2\n"
-     ]
-    }
-   ],
-   "source": [
-    "from CyRK import pysolve_ivp, WrapCySolverResult, __version__\n",
-    "\n",
-    "print(__version__)\n",
-    "\n",
-    "import Cython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "183cb38a-cb77-4e68-8c0e-3457d4772c22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext cython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5af7593-8cc6-4816-8ca7-dc7f4f649936",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content of stdout:\n",
-      "_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\dense.cpp(155): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(76): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(122): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(234): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(286): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(323): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolution.cpp(425): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(239): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(318): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(326): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(332): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(344): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolver.cpp(550): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(54): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(56): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(61): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(80): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(91): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(173): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(239): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(457): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(523): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(587): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(592): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(616): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(634): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\rk.cpp(1000): warning C5051: attribute [[unlikely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\anaconda3\\envs\\cyrk11py311\\Lib\\site-packages\\CyRK\\cy\\cysolve.cpp(228): warning C5051: attribute [[likely]] requires at least '/std:c++20'; ignored\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp(22306): warning C4477: 'printf' : format string '%d' requires an argument of type 'int', but variadic argument 1 has type 'size_t'\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp(22306): note: consider using '%zd' in the format string\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp(22426): warning C4477: 'printf' : format string '%d' requires an argument of type 'int', but variadic argument 1 has type 'size_t'\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp(22426): note: consider using '%zd' in the format string\n",
-      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cpp(25362): warning C4551: function call missing argument list\n",
-      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cp311-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.cp311-win_amd64.exp\n",
-      "Generating code\n",
-      "Finished generating code"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "}\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">014</span>:     <span class=\"c\"># Unpack args</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">015</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">args_dbl_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">double</span><span class=\"o\">*&gt;</span><span class=\"n\">args_ptr</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_args_dbl_ptr = ((double *)__pyx_v_args_ptr);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">016</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">a</span> <span class=\"o\">=</span> <span class=\"n\">args_dbl_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_a = (__pyx_v_args_dbl_ptr[0]);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">017</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">b</span> <span class=\"o\">=</span> <span class=\"n\">args_dbl_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_b = (__pyx_v_args_dbl_ptr[1]);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">018</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">c</span> <span class=\"o\">=</span> <span class=\"n\">args_dbl_ptr</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_c = (__pyx_v_args_dbl_ptr[2]);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">019</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">d</span> <span class=\"o\">=</span> <span class=\"n\">args_dbl_ptr</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_d = (__pyx_v_args_dbl_ptr[3]);\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">020</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">021</span>:     <span class=\"k\">if</span> <span class=\"n\">t</span> <span class=\"o\">==</span> <span class=\"mf\">4.38</span><span class=\"p\">:</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_1 = (__pyx_v_t == 4.38);\n",
-       "  if (__pyx_t_1) {\n",
-       "/* … */\n",
-       "  }\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">022</span>:         <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;</span><span class=\"se\">\\t</span><span class=\"s\">DIFFEQ! args ptr = %p. a = </span><span class=\"si\">%f</span><span class=\"s\">, b = </span><span class=\"si\">%f</span><span class=\"s\">, c = </span><span class=\"si\">%f</span><span class=\"s\">, d = </span><span class=\"si\">%f</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">args_dbl_ptr</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"p\">,</span> <span class=\"n\">b</span><span class=\"p\">,</span> <span class=\"n\">c</span><span class=\"p\">,</span> <span class=\"n\">d</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"\\tDIFFEQ! args ptr = %p. a = %f, b = %f, c = %f, d = %f\\n\"), __pyx_v_args_dbl_ptr, __pyx_v_a, __pyx_v_b, __pyx_v_c, __pyx_v_d));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">023</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">024</span>:     <span class=\"c\"># Unpack y</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">025</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">y0</span><span class=\"p\">,</span> <span class=\"nf\">y1</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">026</span>:     <span class=\"n\">y0</span> <span class=\"o\">=</span> <span class=\"n\">y_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_y0 = (__pyx_v_y_ptr[0]);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">027</span>:     <span class=\"n\">y1</span> <span class=\"o\">=</span> <span class=\"n\">y_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_y1 = (__pyx_v_y_ptr[1]);\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">028</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">029</span>:     <span class=\"n\">dy_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">a</span> <span class=\"o\">*</span> <span class=\"n\">y0</span> <span class=\"o\">-</span> <span class=\"n\">b</span> <span class=\"o\">*</span> <span class=\"n\">y0</span> <span class=\"o\">*</span> <span class=\"n\">y1</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_dy_ptr[0]) = ((__pyx_v_a * __pyx_v_y0) - ((__pyx_v_b * __pyx_v_y0) * __pyx_v_y1));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">030</span>:     <span class=\"n\">dy_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">c</span> <span class=\"o\">*</span> <span class=\"n\">y1</span> <span class=\"o\">+</span> <span class=\"n\">d</span> <span class=\"o\">*</span> <span class=\"n\">y0</span> <span class=\"o\">*</span> <span class=\"n\">y1</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_dy_ptr[1]) = (((-__pyx_v_c) * __pyx_v_y1) + ((__pyx_v_d * __pyx_v_y0) * __pyx_v_y1));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">031</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">032</span>:     <span class=\"n\">dy_ptr</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">a</span> <span class=\"o\">+</span> <span class=\"n\">b</span> <span class=\"o\">+</span> <span class=\"n\">c</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_dy_ptr[2]) = ((__pyx_v_a + __pyx_v_b) + __pyx_v_c);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">033</span>:     <span class=\"n\">dy_ptr</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">b</span> <span class=\"o\">+</span> <span class=\"mf\">2.0</span> <span class=\"o\">*</span> <span class=\"n\">c</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_dy_ptr[3]) = (__pyx_v_b + (2.0 * __pyx_v_c));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">034</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">035</span>: <span class=\"k\">cdef</span> <span class=\"kt\">DiffeqFuncType</span> <span class=\"nf\">diffeq</span> <span class=\"o\">=</span> <span class=\"n\">lotkavolterra_diffeq</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_diffeq = __pyx_f_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_lotkavolterra_diffeq;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">036</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">037</span>: </pre>\n",
-       "<pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">038</span>: <span class=\"k\">cdef</span> <span class=\"kt\">CySolveOutput</span> <span class=\"nf\">test_1</span><span class=\"p\">():</span></pre>\n",
-       "<pre class='cython code score-2 '>static __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_f_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_test_1(void) {\n",
-       "  double __pyx_v_t_span[2];\n",
-       "  double *__pyx_v_t_span_ptr;\n",
-       "  double __pyx_v_y0[2];\n",
-       "  double *__pyx_v_y0_ptr;\n",
-       "  size_t __pyx_v_num_y;\n",
-       "  std::vector&lt;double&gt;  __pyx_v_args;\n",
-       "  double *__pyx_v_args_ptr;\n",
-       "  __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_v_result;\n",
-       "  __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_r;\n",
-       "/* … */\n",
-       "  /* function exit code */\n",
-       "  __pyx_L1_error:;\n",
-       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33.test_1\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
-       "  __Pyx_pretend_to_initialize(&amp;__pyx_r);\n",
-       "  __pyx_L0:;\n",
-       "  return __pyx_r;\n",
-       "}\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">039</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">040</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[2] <span class=\"nf\">t_span</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"mf\">15.</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_1[0] = 0.;\n",
-       "  __pyx_t_1[1] = 15.;\n",
-       "  memcpy(&amp;(__pyx_v_t_span[0]), __pyx_t_1, sizeof(__pyx_v_t_span[0]) * (2));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">041</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">t_span_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&amp;</span><span class=\"n\">t_span</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_t_span_ptr = (&amp;(__pyx_v_t_span[0]));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">042</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">043</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[2] <span class=\"nf\">y0</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">10.</span><span class=\"p\">,</span> <span class=\"mf\">5.</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_2[0] = 10.;\n",
-       "  __pyx_t_2[1] = 5.;\n",
-       "  memcpy(&amp;(__pyx_v_y0[0]), __pyx_t_2, sizeof(__pyx_v_y0[0]) * (2));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">044</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">y0_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&amp;</span><span class=\"n\">y0</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_y0_ptr = (&amp;(__pyx_v_y0[0]));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">045</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">046</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">size_t</span> <span class=\"nf\">num_y</span> <span class=\"o\">=</span> <span class=\"mf\">2</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_num_y = 2;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">047</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">048</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">vector</span>[<span class=\"kt\">double</span>] <span class=\"nf\">args</span> <span class=\"o\">=</span> <span class=\"n\">vector</span><span class=\"p\">[</span><span class=\"n\">double</span><span class=\"p\">]()</span></pre>\n",
-       "<pre class='cython code score-0 '>  try {\n",
-       "    __pyx_t_3 = std::vector&lt;double&gt; ();\n",
-       "  } catch(...) {\n",
-       "    __Pyx_CppExn2PyErr();\n",
-       "    <span class='error_goto'>__PYX_ERR(0, 48, __pyx_L1_error)</span>\n",
-       "  }\n",
-       "  __pyx_v_args = __PYX_STD_MOVE_IF_SUPPORTED(__pyx_t_3);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">049</span>:     <span class=\"n\">args</span><span class=\"o\">.</span><span class=\"n\">resize</span><span class=\"p\">(</span><span class=\"mf\">4</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  try {\n",
-       "    __pyx_v_args.resize(4);\n",
-       "  } catch(...) {\n",
-       "    __Pyx_CppExn2PyErr();\n",
-       "    <span class='error_goto'>__PYX_ERR(0, 49, __pyx_L1_error)</span>\n",
-       "  }\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">050</span>:     <span class=\"n\">args</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.5</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args[0]) = 1.5;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">051</span>:     <span class=\"n\">args</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args[1]) = 1.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">052</span>:     <span class=\"n\">args</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">3.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args[2]) = 3.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">053</span>:     <span class=\"n\">args</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args[3]) = 1.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">054</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">args_ptr</span> <span class=\"o\">=</span> <span class=\"n\">args</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">()</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_args_ptr = __pyx_v_args.data();\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">055</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">056</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">CySolveOutput</span> <span class=\"nf\">result</span> <span class=\"o\">=</span> <span class=\"n\">cysolve_ivp</span><span class=\"p\">(</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_5.__pyx_n = 17;\n",
-       "  __pyx_t_5.method = 1;\n",
-       "  __pyx_t_5.rtol = 1.0e-2;\n",
-       "  __pyx_t_5.atol = 1.0e-3;\n",
-       "  __pyx_t_5.args_ptr = __pyx_v_args_ptr;\n",
-       "  __pyx_t_5.size_of_args = ((sizeof(double)) * 4);\n",
-       "  __pyx_t_5.num_extra = 2;\n",
-       "  __pyx_t_5.max_num_steps = 0xF4240;\n",
-       "  __pyx_t_5.max_ram_MB = 0x7D0;\n",
-       "  __pyx_t_5.dense_output = 1;\n",
-       "  __pyx_t_5.t_eval = NULL;\n",
-       "  __pyx_t_5.len_t_eval = 0;\n",
-       "  __pyx_t_5.pre_eval_func = NULL;\n",
-       "  __pyx_t_5.rtols_ptr = NULL;\n",
-       "  __pyx_t_5.atols_ptr = NULL;\n",
-       "  __pyx_t_5.max_step = 10000.0;\n",
-       "  __pyx_t_5.first_step = 0.0;\n",
-       "  __pyx_t_5.expected_size = 0x64;\n",
-       "  __pyx_t_4 = __pyx_f_4CyRK_2cy_12cysolver_api_cysolve_ivp(__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_diffeq, __pyx_v_t_span_ptr, __pyx_v_y0_ptr, __pyx_v_num_y, &amp;__pyx_t_5); \n",
-       "  __pyx_v_result = __PYX_STD_MOVE_IF_SUPPORTED(__pyx_t_4);\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">057</span>:         <span class=\"n\">diffeq</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">058</span>:         <span class=\"n\">t_span_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">059</span>:         <span class=\"n\">y0_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">060</span>:         <span class=\"n\">num_y</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">061</span>:         <span class=\"mf\">1</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">062</span>:         <span class=\"mf\">1.0e-2</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">063</span>:         <span class=\"mf\">1.0e-3</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">064</span>:         <span class=\"n\">args_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">065</span>:         <span class=\"n\">sizeof</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">4</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">066</span>:         <span class=\"mf\">2</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">067</span>:         <span class=\"mf\">1</span><span class=\"n\">_000_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">068</span>:         <span class=\"mf\">2</span><span class=\"n\">_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">069</span>:         <span class=\"bp\">True</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">070</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">071</span>:         <span class=\"mf\">0</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">072</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">073</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">074</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">075</span>:         <span class=\"mf\">10</span><span class=\"n\">_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">076</span>:         <span class=\"mf\">0.0</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">077</span>:         <span class=\"mf\">100</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">078</span>:         <span class=\"p\">)</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">079</span>:     <span class=\"k\">return</span> <span class=\"n\">result</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_r = __pyx_v_result;\n",
-       "  goto __pyx_L0;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">080</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">081</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.stdlib</span> <span class=\"k\">cimport</span> <span class=\"n\">malloc</span><span class=\"p\">,</span> <span class=\"n\">free</span><span class=\"p\">,</span> <span class=\"n\">realloc</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">082</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">083</span>: <span class=\"k\">cdef</span> <span class=\"kt\">CySolveOutput</span> <span class=\"nf\">test_2</span><span class=\"p\">():</span></pre>\n",
-       "<pre class='cython code score-0 '>static __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_f_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_test_2(void) {\n",
-       "  double __pyx_v_t_span[2];\n",
-       "  double *__pyx_v_t_span_ptr;\n",
-       "  double __pyx_v_y0[2];\n",
-       "  double *__pyx_v_y0_ptr;\n",
-       "  size_t __pyx_v_num_y;\n",
-       "  size_t __pyx_v_arg_size;\n",
-       "  double *__pyx_v_args_ptr;\n",
-       "  __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_v_result;\n",
-       "  size_t __pyx_v_i;\n",
-       "  __pyx_t_4CyRK_2cy_12cysolver_api_CySolveOutput __pyx_r;\n",
-       "/* … */\n",
-       "  /* function exit code */\n",
-       "  __pyx_L0:;\n",
-       "  return __pyx_r;\n",
-       "}\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">084</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">085</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[2] <span class=\"nf\">t_span</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"mf\">10.</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_1[0] = 0.;\n",
-       "  __pyx_t_1[1] = 10.;\n",
-       "  memcpy(&amp;(__pyx_v_t_span[0]), __pyx_t_1, sizeof(__pyx_v_t_span[0]) * (2));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">086</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">t_span_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&amp;</span><span class=\"n\">t_span</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_t_span_ptr = (&amp;(__pyx_v_t_span[0]));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">087</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">088</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[2] <span class=\"nf\">y0</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">10.</span><span class=\"p\">,</span> <span class=\"mf\">5.</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_2[0] = 10.;\n",
-       "  __pyx_t_2[1] = 5.;\n",
-       "  memcpy(&amp;(__pyx_v_y0[0]), __pyx_t_2, sizeof(__pyx_v_y0[0]) * (2));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">089</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">y0_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&amp;</span><span class=\"n\">y0</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_y0_ptr = (&amp;(__pyx_v_y0[0]));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">090</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">091</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">size_t</span> <span class=\"nf\">num_y</span> <span class=\"o\">=</span> <span class=\"mf\">2</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_num_y = 2;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">092</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">093</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">size_t</span> <span class=\"nf\">arg_size</span> <span class=\"o\">=</span> <span class=\"n\">sizeof</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"mf\">4</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_arg_size = ((sizeof(double)) * 4);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">094</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">args_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">double</span><span class=\"o\">*&gt;</span><span class=\"n\">malloc</span><span class=\"p\">(</span><span class=\"n\">arg_size</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_args_ptr = ((double *)malloc(__pyx_v_arg_size));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">095</span>:     <span class=\"n\">args_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.5</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args_ptr[0]) = 1.5;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">096</span>:     <span class=\"n\">args_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args_ptr[1]) = 1.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">097</span>:     <span class=\"n\">args_ptr</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">3.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args_ptr[2]) = 3.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">098</span>:     <span class=\"n\">args_ptr</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span></pre>\n",
-       "<pre class='cython code score-0 '>  (__pyx_v_args_ptr[3]) = 1.0;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">099</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">100</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 1</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 1\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">101</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 1; args = %p</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">args_ptr</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 1; args = %p\\n\"), __pyx_v_args_ptr));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">102</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">CySolveOutput</span> <span class=\"nf\">result</span> <span class=\"o\">=</span> <span class=\"n\">cysolve_ivp</span><span class=\"p\">(</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_t_4.__pyx_n = 17;\n",
-       "  __pyx_t_4.method = 1;\n",
-       "  __pyx_t_4.rtol = 1.0e-2;\n",
-       "  __pyx_t_4.atol = 1.0e-3;\n",
-       "  __pyx_t_4.args_ptr = __pyx_v_args_ptr;\n",
-       "  __pyx_t_4.size_of_args = __pyx_v_arg_size;\n",
-       "  __pyx_t_4.num_extra = 2;\n",
-       "  __pyx_t_4.max_num_steps = 0xF4240;\n",
-       "  __pyx_t_4.max_ram_MB = 0x7D0;\n",
-       "  __pyx_t_4.dense_output = 1;\n",
-       "  __pyx_t_4.t_eval = NULL;\n",
-       "  __pyx_t_4.len_t_eval = 0;\n",
-       "  __pyx_t_4.pre_eval_func = NULL;\n",
-       "  __pyx_t_4.rtols_ptr = NULL;\n",
-       "  __pyx_t_4.atols_ptr = NULL;\n",
-       "  __pyx_t_4.max_step = 10000.0;\n",
-       "  __pyx_t_4.first_step = 0.0;\n",
-       "  __pyx_t_4.expected_size = 0x64;\n",
-       "  __pyx_t_3 = __pyx_f_4CyRK_2cy_12cysolver_api_cysolve_ivp(__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_diffeq, __pyx_v_t_span_ptr, __pyx_v_y0_ptr, __pyx_v_num_y, &amp;__pyx_t_4); \n",
-       "  __pyx_v_result = __PYX_STD_MOVE_IF_SUPPORTED(__pyx_t_3);\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">103</span>:         <span class=\"n\">diffeq</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">104</span>:         <span class=\"n\">t_span_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">105</span>:         <span class=\"n\">y0_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">106</span>:         <span class=\"n\">num_y</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">107</span>:         <span class=\"mf\">1</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">108</span>:         <span class=\"mf\">1.0e-2</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">109</span>:         <span class=\"mf\">1.0e-3</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">110</span>:         <span class=\"n\">args_ptr</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">111</span>:         <span class=\"n\">arg_size</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">112</span>:         <span class=\"mf\">2</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">113</span>:         <span class=\"mf\">1</span><span class=\"n\">_000_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">114</span>:         <span class=\"mf\">2</span><span class=\"n\">_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">115</span>:         <span class=\"bp\">True</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">116</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">117</span>:         <span class=\"mf\">0</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">118</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">119</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">120</span>:         <span class=\"bp\">NULL</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">121</span>:         <span class=\"mf\">10</span><span class=\"n\">_000</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">122</span>:         <span class=\"mf\">0.0</span><span class=\"p\">,</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">123</span>:         <span class=\"mf\">100</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">124</span>:         <span class=\"p\">)</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">125</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 2</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 2\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">126</span>:     <span class=\"n\">args_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">double</span><span class=\"o\">*&gt;</span><span class=\"n\">realloc</span><span class=\"p\">(</span><span class=\"n\">args_ptr</span><span class=\"p\">,</span> <span class=\"n\">sizeof</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"mf\">3000</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_args_ptr = ((double *)realloc(__pyx_v_args_ptr, ((sizeof(double)) * 0xBB8)));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">127</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 3</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 3\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">128</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">size_t</span> <span class=\"nf\">i</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">129</span>:     <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">3000</span><span class=\"p\">):</span></pre>\n",
-       "<pre class='cython code score-0 '>  for (__pyx_t_5 = 0; __pyx_t_5 &lt; 0xBB8; __pyx_t_5+=1) {\n",
-       "    __pyx_v_i = __pyx_t_5;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">130</span>:         <span class=\"n\">args_ptr</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mf\">99.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_args_ptr[__pyx_v_i]) = -99.0;\n",
-       "  }\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">131</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 4; args = %p</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">args_ptr</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 4; args = %p\\n\"), __pyx_v_args_ptr));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">132</span>:     <span class=\"n\">free</span><span class=\"p\">(</span><span class=\"n\">args_ptr</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  free(__pyx_v_args_ptr);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">133</span>:     <span class=\"n\">args_ptr</span> <span class=\"o\">=</span> <span class=\"bp\">NULL</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_args_ptr = NULL;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">134</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Pt 5</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"Test 2 Pt 5\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">135</span>:     <span class=\"k\">return</span> <span class=\"n\">result</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_r = __pyx_v_result;\n",
-       "  goto __pyx_L0;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">136</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">137</span>: <span class=\"k\">cdef</span> <span class=\"kt\">CySolveOutput</span> <span class=\"nf\">res_shptr</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">138</span>: <span class=\"k\">cdef</span> <span class=\"kt\">CySolverResult</span>* <span class=\"nf\">res</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">139</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">140</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.stdio</span> <span class=\"k\">cimport</span> <span class=\"n\">printf</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">141</span>: </pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">142</span>: <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[4] <span class=\"nf\">y_interp</span></pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">143</span>: <span class=\"k\">cdef</span> <span class=\"kt\">double</span>* <span class=\"nf\">y_interp_ptr</span> <span class=\"o\">=</span> <span class=\"o\">&amp;</span><span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
-       "<pre class='cython code score-0 '>  __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr = (&amp;(__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[0]));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">144</span>: <span class=\"k\">cdef</span> <span class=\"kt\">size_t</span> <span class=\"nf\">i</span></pre>\n",
-       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">145</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">146</span>: <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;</span><span class=\"se\">\\n</span><span class=\"s\">Test 1</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"\\nTest 1\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">147</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">10</span><span class=\"p\">):</span></pre>\n",
-       "<pre class='cython code score-0 '>  for (__pyx_t_10 = 0; __pyx_t_10 &lt; 10; __pyx_t_10+=1) {\n",
-       "    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_i = __pyx_t_10;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">148</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;</span><span class=\"se\">\\t</span><span class=\"s\">SubTest Num = </span><span class=\"si\">%d</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"\\tSubTest Num = %d\\n\"), __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_i));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">149</span>:     <span class=\"n\">res_shptr</span> <span class=\"o\">=</span> <span class=\"n\">test_1</span><span class=\"p\">()</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_t_11 = __pyx_f_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_test_1();<span class='error_goto'> if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 149, __pyx_L1_error)</span>\n",
-       "    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res_shptr = __PYX_STD_MOVE_IF_SUPPORTED(__pyx_t_11);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">150</span>:     <span class=\"n\">res</span> <span class=\"o\">=</span> <span class=\"n\">res_shptr</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">()</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res = __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res_shptr.get();\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">151</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 1 Success = </span><span class=\"si\">%d</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">res</span><span class=\"o\">.</span><span class=\"n\">success</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 1 Success = %d\\n\"), __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res-&gt;success));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">152</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">153</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[0]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">154</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[1]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">155</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[2]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">156</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[3]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">157</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">158</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 1; calling</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 1; calling\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">159</span>:     <span class=\"n\">res</span><span class=\"o\">.</span><span class=\"n\">call</span><span class=\"p\">(</span><span class=\"mf\">4.38</span><span class=\"p\">,</span> <span class=\"n\">y_interp</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res-&gt;call(4.38, __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">160</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 1; Call Finished. y0 = </span><span class=\"si\">%e</span><span class=\"s\">; y1 = </span><span class=\"si\">%e</span><span class=\"s\">; y2 = </span><span class=\"si\">%e</span><span class=\"s\">; y3 = </span><span class=\"si\">%e</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">])</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 1; Call Finished. y0 = %e; y1 = %e; y2 = %e; y3 = %e\\n\"), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[0]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[1]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[2]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[3])));\n",
-       "  }\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">161</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">162</span>: <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;</span><span class=\"se\">\\n</span><span class=\"s\">Test 2</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>  (void)(printf(((char const *)\"\\nTest 2\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">163</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">10</span><span class=\"p\">):</span></pre>\n",
-       "<pre class='cython code score-0 '>  for (__pyx_t_10 = 0; __pyx_t_10 &lt; 10; __pyx_t_10+=1) {\n",
-       "    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_i = __pyx_t_10;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">164</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;</span><span class=\"se\">\\t</span><span class=\"s\">SubTest Num = </span><span class=\"si\">%d</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"\\tSubTest Num = %d\\n\"), __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_i));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">165</span>:     <span class=\"n\">res_shptr</span> <span class=\"o\">=</span> <span class=\"n\">test_2</span><span class=\"p\">()</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_t_11 = __pyx_f_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_test_2();<span class='error_goto'> if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 165, __pyx_L1_error)</span>\n",
-       "    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res_shptr = __PYX_STD_MOVE_IF_SUPPORTED(__pyx_t_11);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">166</span>:     <span class=\"n\">res</span> <span class=\"o\">=</span> <span class=\"n\">res_shptr</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">()</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res = __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res_shptr.get();\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">167</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2 Success = </span><span class=\"si\">%d</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">res</span><span class=\"o\">.</span><span class=\"n\">success</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 2 Success = %d\\n\"), __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res-&gt;success));\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">168</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">169</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[0]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">170</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[1]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">171</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[2]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">172</span>:     <span class=\"n\">y_interp_ptr</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span></pre>\n",
-       "<pre class='cython code score-0 '>    (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp_ptr[3]) = 0.0;\n",
-       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">173</span>: </pre>\n",
-       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">174</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2; calling</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 2; calling\\n\")));\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">175</span>:     <span class=\"n\">res</span><span class=\"o\">.</span><span class=\"n\">call</span><span class=\"p\">(</span><span class=\"mf\">4.38</span><span class=\"p\">,</span> <span class=\"n\">y_interp</span><span class=\"p\">)</span></pre>\n",
-       "<pre class='cython code score-0 '>    __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_res-&gt;call(4.38, __pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp);\n",
-       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">176</span>:     <span class=\"n\">printf</span><span class=\"p\">(</span><span class=\"s\">&quot;Test 2; Call Finished. y0 = </span><span class=\"si\">%e</span><span class=\"s\">; y1 = </span><span class=\"si\">%e</span><span class=\"s\">; y2 = </span><span class=\"si\">%e</span><span class=\"s\">; y3 = </span><span class=\"si\">%e</span><span class=\"se\">\\n</span><span class=\"s\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">y_interp</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">])</span></pre>\n",
-       "<pre class='cython code score-0 '>    (void)(printf(((char const *)\"Test 2; Call Finished. y0 = %e; y1 = %e; y2 = %e; y3 = %e\\n\"), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[0]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[1]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[2]), (__pyx_v_54_cython_magic_c0c2a34c718801b06ca16ae83c6a67ae4f243f33_y_interp[3])));\n",
-       "  }\n",
-       "</pre></div></body></html>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%cython -a -f\n",
-    "# distutils: language = c++\n",
-    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
-    "\n",
-    "import numpy as np\n",
-    "cimport numpy as np\n",
-    "np.import_array()\n",
-    "\n",
-    "from libcpp.vector cimport vector\n",
-    "\n",
-    "from CyRK cimport cysolve_ivp, CySolveOutput, CySolverResult, DiffeqFuncType, PreEvalFunc\n",
-    "\n",
-    "\n",
-    "cdef void lotkavolterra_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
-    "    # Unpack args\n",
-    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
-    "    cdef double a = args_dbl_ptr[0]\n",
-    "    cdef double b = args_dbl_ptr[1]\n",
-    "    cdef double c = args_dbl_ptr[2]\n",
-    "    cdef double d = args_dbl_ptr[3]\n",
-    "\n",
-    "    if t == 4.38:\n",
-    "        printf(\"\\tDIFFEQ! args ptr = %p. a = %f, b = %f, c = %f, d = %f\\n\", args_dbl_ptr, a, b, c, d)\n",
-    "\n",
-    "    # Unpack y\n",
-    "    cdef double y0, y1\n",
-    "    y0 = y_ptr[0]\n",
-    "    y1 = y_ptr[1]\n",
-    "\n",
-    "    dy_ptr[0] = a * y0 - b * y0 * y1\n",
-    "    dy_ptr[1] = -c * y1 + d * y0 * y1\n",
-    "\n",
-    "    dy_ptr[2] = a + b + c\n",
-    "    dy_ptr[3] = b + 2.0 * c\n",
-    "\n",
-    "cdef DiffeqFuncType diffeq = lotkavolterra_diffeq\n",
-    "\n",
-    "\n",
-    "cdef CySolveOutput test_1():\n",
-    "\n",
-    "    cdef double[2] t_span = [0., 15.]\n",
-    "    cdef double* t_span_ptr = &t_span[0]\n",
-    "\n",
-    "    cdef double[2] y0 = [10., 5.]\n",
-    "    cdef double* y0_ptr = &y0[0]\n",
-    "\n",
-    "    cdef size_t num_y = 2\n",
-    "\n",
-    "    cdef vector[double] args = vector[double]()\n",
-    "    args.resize(4)\n",
-    "    args[0] = 1.5\n",
-    "    args[1] = 1.0\n",
-    "    args[2] = 3.0\n",
-    "    args[3] = 1.0\n",
-    "    cdef double* args_ptr = args.data()\n",
-    "\n",
-    "    cdef CySolveOutput result = cysolve_ivp(\n",
-    "        diffeq,\n",
-    "        t_span_ptr,\n",
-    "        y0_ptr,\n",
-    "        num_y,\n",
-    "        1,\n",
-    "        1.0e-2,\n",
-    "        1.0e-3,\n",
-    "        args_ptr,\n",
-    "        sizeof(double) * 4,\n",
-    "        2,\n",
-    "        1_000_000,\n",
-    "        2_000,\n",
-    "        True,\n",
-    "        NULL,\n",
-    "        0,\n",
-    "        NULL,\n",
-    "        NULL,\n",
-    "        NULL,\n",
-    "        10_000,\n",
-    "        0.0,\n",
-    "        100\n",
-    "        )\n",
-    "    return result\n",
-    "\n",
-    "from libc.stdlib cimport malloc, free, realloc\n",
-    "\n",
-    "cdef CySolveOutput test_2():\n",
-    "\n",
-    "    cdef double[2] t_span = [0., 10.]\n",
-    "    cdef double* t_span_ptr = &t_span[0]\n",
-    "\n",
-    "    cdef double[2] y0 = [10., 5.]\n",
-    "    cdef double* y0_ptr = &y0[0]\n",
-    "\n",
-    "    cdef size_t num_y = 2\n",
-    "\n",
-    "    cdef size_t arg_size = sizeof(double)*4\n",
-    "    cdef double* args_ptr = <double*>malloc(arg_size)\n",
-    "    args_ptr[0] = 1.5\n",
-    "    args_ptr[1] = 1.0\n",
-    "    args_ptr[2] = 3.0\n",
-    "    args_ptr[3] = 1.0\n",
-    "\n",
-    "    printf(\"Test 2 Pt 1\\n\")\n",
-    "    printf(\"Test 2 Pt 1; args = %p\\n\", args_ptr)\n",
-    "    cdef CySolveOutput result = cysolve_ivp(\n",
-    "        diffeq,\n",
-    "        t_span_ptr,\n",
-    "        y0_ptr,\n",
-    "        num_y,\n",
-    "        1,\n",
-    "        1.0e-2,\n",
-    "        1.0e-3,\n",
-    "        args_ptr,\n",
-    "        arg_size,\n",
-    "        2,\n",
-    "        1_000_000,\n",
-    "        2_000,\n",
-    "        True,\n",
-    "        NULL,\n",
-    "        0,\n",
-    "        NULL,\n",
-    "        NULL,\n",
-    "        NULL,\n",
-    "        10_000,\n",
-    "        0.0,\n",
-    "        100\n",
-    "        )\n",
-    "    printf(\"Test 2 Pt 2\\n\")\n",
-    "    args_ptr = <double*>realloc(args_ptr, sizeof(double)*3000)\n",
-    "    printf(\"Test 2 Pt 3\\n\")\n",
-    "    cdef size_t i \n",
-    "    for i in range(3000):\n",
-    "        args_ptr[i] = -99.0\n",
-    "    printf(\"Test 2 Pt 4; args = %p\\n\", args_ptr)\n",
-    "    free(args_ptr)\n",
-    "    args_ptr = NULL\n",
-    "    printf(\"Test 2 Pt 5\\n\")\n",
-    "    return result\n",
-    "\n",
-    "cdef CySolveOutput res_shptr\n",
-    "cdef CySolverResult* res\n",
-    "\n",
-    "from libc.stdio cimport printf\n",
-    "\n",
-    "cdef double[4] y_interp\n",
-    "cdef double* y_interp_ptr = &y_interp[0]\n",
-    "cdef size_t i\n",
-    "\n",
-    "printf(\"\\nTest 1\\n\")\n",
-    "for i in range(10):\n",
-    "    printf(\"\\tSubTest Num = %d\\n\", i)\n",
-    "    res_shptr = test_1()\n",
-    "    res = res_shptr.get()\n",
-    "    printf(\"Test 1 Success = %d\\n\", res.success)\n",
-    "    \n",
-    "    y_interp_ptr[0] = 0.0\n",
-    "    y_interp_ptr[1] = 0.0\n",
-    "    y_interp_ptr[2] = 0.0\n",
-    "    y_interp_ptr[3] = 0.0\n",
-    "    \n",
-    "    printf(\"Test 1; calling\\n\")\n",
-    "    res.call(4.38, y_interp)\n",
-    "    printf(\"Test 1; Call Finished. y0 = %e; y1 = %e; y2 = %e; y3 = %e\\n\", y_interp[0], y_interp[1], y_interp[2], y_interp[3])\n",
-    "\n",
-    "printf(\"\\nTest 2\\n\")\n",
-    "for i in range(10):\n",
-    "    printf(\"\\tSubTest Num = %d\\n\", i)\n",
-    "    res_shptr = test_2()\n",
-    "    res = res_shptr.get()\n",
-    "    printf(\"Test 2 Success = %d\\n\", res.success)\n",
-    "    \n",
-    "    y_interp_ptr[0] = 0.0\n",
-    "    y_interp_ptr[1] = 0.0\n",
-    "    y_interp_ptr[2] = 0.0\n",
-    "    y_interp_ptr[3] = 0.0\n",
-    "    \n",
-    "    printf(\"Test 2; calling\\n\")\n",
-    "    res.call(4.38, y_interp)\n",
-    "    printf(\"Test 2; Call Finished. y0 = %e; y1 = %e; y2 = %e; y3 = %e\\n\", y_interp[0], y_interp[1], y_interp[2], y_interp[3])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index 68e61ad..cd6f154 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name='CyRK'
-version = '0.12.0a0.dev5'
+version = '0.12.0'
 description='Runge-Kutta ODE Integrator Implemented in Cython and Numba.'
 authors= [
     {name = 'Joe P. Renaud', email = 'joe.p.renaud@gmail.com'}