code: def _transpose_out_pad(
      x_shape,
      filter_shape,
      strides,
      padding,
      dims,
      dilations,
      data_format,
  ):
      if data_format[-1] == "C":
          offset = 1
      else:
          offset = 2
      dilations = (
          [dilations] * dims
          if isinstance(dilations, int)
          else dilations
      )
      strides = (
          [strides] * dims if isinstance(strides, int) else strides
      )
      if isinstance(padding, str):
          out_shape = [
              _deconv_length(
                  x_shape[offset + i],
                  strides[i],
                  filter_shape[i],
                  padding,
                  dilations[i],
              )
              for i in range(dims)
          ]
      else:
          if isinstance(padding, int):
              padding = [[padding, padding]] * dims
          out_shape = [
              (x_shape[offset + i] - 1) * strides[i]
              - padding[i][0]
              - padding[i][1]
              + dilations[i] * (filter_shape[i] - 1)
              + 1
              for i in range(dims)
          ]
          if data_format[-1] == "C":
              padding = [[0, 0], *padding, [0, 0]]
          else:
              padding = [[0, 0], [0, 0], *padding]
      if data_format[-1] == "C":
          out_shape = [x_shape[0], *out_shape, filter_shape[-2]]
      else:
          out_shape = [x_shape[0], filter_shape[-2], *out_shape]
      return out_shape, padding
  
imports: from typing import Optional, Tuple, Union, Sequence
  import tensorflow
  from tensorflow.python.types.core import Tensor
  import ivy
  from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _deconv_length, _get_x_data_format
label_desc: Use image node_id 5 for calling a global function with example usage: _transpose_out_pad(x_shape, filter_shape, strides, padding, dims, dilations, data_format) and returns: out_shape,  padding