code: def _get_transpose_pad(padding, output_padding, dims):
      (
          padding,
          output_padding,
      ) = map(
          lambda x: [x] * dims if isinstance(x, int) else x,
          [padding, output_padding],
      )
      asymmetric_padding = [
          [pad, pad - output_pad]
          for pad, output_pad in zip(padding, output_padding)
      ]
      return asymmetric_padding
  
file_path: ivy/ivy/functional/frontends/torch/nn/functional/convolution_functions.py
imports: import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 3 for calling a global function with example usage: _get_transpose_pad(padding, output_padding, dims) and returns: asymmetric_padding