diff --git a/einconv/expressions/convNd_kfac_reduce.py b/einconv/expressions/convNd_kfac_reduce.py
index 3fa55ee..dd6d766 100644
--- a/einconv/expressions/convNd_kfac_reduce.py
+++ b/einconv/expressions/convNd_kfac_reduce.py
@@ -68,8 +68,8 @@ def einsum_expression(
         Einsum equation
         Einsum operands in order un-grouped input, patterns, un-grouped input, \
         patterns, normalization scaling
-        Output shape: ``[groups, in_channels //groups * tot_kernel_sizes,\
-        in_channels //groups * tot_kernel_sizes]``
+        Output shape: ``[groups, in_channels // groups * tot_kernel_sizes,\
+        in_channels // groups * tot_kernel_sizes]``
     """
     N = x.dim() - 2
 
diff --git a/einconv/expressions/conv_transposeNd_kfac_reduce.py b/einconv/expressions/conv_transposeNd_kfac_reduce.py
index 1bae4a0..450368c 100644
--- a/einconv/expressions/conv_transposeNd_kfac_reduce.py
+++ b/einconv/expressions/conv_transposeNd_kfac_reduce.py
@@ -85,8 +85,8 @@ def einsum_expression(
         Einsum equation
         Einsum operands in order un-grouped input, patterns, un-grouped input, \
         patterns, normalization scaling
-        Output shape: `[groups, out_channels //groups * tot_kernel_sizes,\
-        out_channels //groups * tot_kernel_sizes]`
+        Output shape: `[groups, out_channels // groups * tot_kernel_sizes,\
+        out_channels // groups * tot_kernel_sizes]`
     """
     N = x.dim() - 2
 
@@ -116,8 +116,8 @@ def einsum_expression(
     if output_size is not None:
         t_output_size = _tuple(output_size, N)
         t_output_padding = tuple(
-            output_size - get_conv_input_size(conv_out_size, K, S, P, 0, D)
-            for output_size, conv_out_size, K, S, P, D in zip(
+            output_size - get_conv_input_size(I, K, S, P, 0, D)
+            for output_size, I, K, S, P, D in zip(
                 t_output_size,
                 conv_output_size,
                 t_kernel_size,
@@ -130,8 +130,8 @@ def einsum_expression(
         t_output_padding = _tuple(output_padding, N)
 
     conv_input_size = tuple(
-        get_conv_input_size(output_size, K, S, P, output_padding, D)
-        for output_size, K, S, P, output_padding, D in zip(
+        get_conv_input_size(O, K, S, P, output_padding, D)
+        for O, K, S, P, output_padding, D in zip(
             conv_output_size,
             t_kernel_size,
             t_stride,
diff --git a/test/functionals/test_unfold_transpose.py b/test/functionals/test_unfold_transpose.py
index 184a282..9c15325 100644
--- a/test/functionals/test_unfold_transpose.py
+++ b/test/functionals/test_unfold_transpose.py
@@ -51,11 +51,9 @@ def test_unfoldNd_transpose(case: Dict, dev: device, simplify: bool):
     inputs = input_fn().to(dev)
 
     result = unfoldNd.unfold_transposeNd(inputs, kernel_size, **kwargs)
-
     einconv_result = unfoldNd_transpose(
         inputs, kernel_size, **kwargs, simplify=simplify
     )
-
     report_nonclose(result, einconv_result)