code: def _get_tranpose_padding(
      x_shape,
      filter_shape,
      strides,
      padding,
      dims,
      dilations,
      output_shape,
  ):
      new_shape = [
          _deconv_length(
              x_shape[i],
              strides[i],
              filter_shape[i],
              padding,
              dilations[i],
          )
          for i in range(dims)
      ]
      if output_shape is None:
          output_shape = [x_shape[0], *new_shape, filter_shape[-1]]
      elif len(output_shape) == dims:
          output_shape = (
              [x_shape[0]] + list(output_shape) + [filter_shape[-1]]
          )
      shape_diff = [
          -(output_shape[1 + i] - new_shape[i]) for i in range(dims)
      ]
      pad_list = [
          _transpose_padding_helper(
              filter_shape[i],
              strides[i],
              padding,
              dilations[i],
              shape_diff[i],
          )
          for i in range(dims)
      ]
      return pad_list
  
file_path: ivy/ivy/functional/backends/jax/layers.py
imports: import jax.lax
  import jax.numpy
  import ivy
  from ivy.functional.backends.jax import JaxArray
  from typing import Union, Tuple, Optional, Sequence
  from ivy.functional.ivy.layers import _handle_padding, _deconv_length, _get_x_data_format
label_desc: Use image node_id 2 for calling a global function with example usage: _get_tranpose_padding(x_shape, filter_shape, strides, padding, dims, dilations, output_shape) and returns: pad_list