[BUG/CLN] Refactor tensor handling and cleanup lazy weight logic

Updated tensor operations in `symbolic_evaluator.py` to use `lazy_weights` consistently, replacing redundant `LazyTensor` calls. Adjusted dtype handling in `backend_tensor.py` for better compatibility with PyTorch.

Author: Leguark

gempy_engine/core/backend_tensor.py

@@ -209,6 +209,8 @@ def _sum(tensor, axis=None, dtype=None, keepdims=False):
                 case pykeops.numpy.LazyTensor() | pykeops.torch.LazyTensor():
                     return tensor.sum(axis)
                 case torch.Tensor() if torch_available:
+                    if isinstance(dtype, str):
+                        dtype = getattr(torch, dtype)
                     return tensor.sum(axis, keepdims=keepdims, dtype=dtype)
                 case _:
                     raise TypeError("Unsupported tensor type")

gempy_engine/modules/evaluator/symbolic_evaluator.py

@@ -34,12 +34,12 @@ def symbolic_evaluator(solver_input: SolverInput, weights: np.ndarray, options:
         eval_gx_kernel = yield_evaluation_grad_kernel(solver_input, options.kernel_options, axis=0)
         eval_gy_kernel = yield_evaluation_grad_kernel(solver_input, options.kernel_options, axis=1)
 
-        gx_field = (eval_gx_kernel.T * LazyTensor(weights, axis=1)).sum(axis=1, backend=backend_string).reshape(-1)
-        gy_field = (eval_gy_kernel.T * LazyTensor(weights, axis=1)).sum(axis=1, backend=backend_string).reshape(-1)
+        gx_field = (eval_gx_kernel.T * lazy_weights).sum(axis=1, backend=backend_string).reshape(-1)
+        gy_field = (eval_gy_kernel.T * lazy_weights).sum(axis=1, backend=backend_string).reshape(-1)
 
         if options.number_dimensions == 3:
             eval_gz_kernel = yield_evaluation_grad_kernel(solver_input, options.kernel_options, axis=2)
-            gz_field = (eval_gz_kernel.T * LazyTensor(weights, axis=1)).sum(axis=1, backend=backend_string).reshape(-1)
+            gz_field = (eval_gz_kernel.T * lazy_weights).sum(axis=1, backend=backend_string).reshape(-1)
         elif options.number_dimensions == 2:
             gz_field = None
         else: