code: def _conv_transpose(
      input,
      weight,
      bias=None,
      stride=1,
      padding=0,
      output_padding=0,
      groups=1,
      dilation=1,
  ):
      dims = len(input.shape) - 2
      weight = ivy.permute_dims(
          weight, axes=(*range(2, dims + 2), 0, 1)
      )
      for i in range(dims):
          weight = ivy.flip(weight, axis=i)
      padding, output_padding, stride, dilation = map(
          lambda x: [x] * dims if isinstance(x, int) else x,
          [padding, output_padding, stride, dilation],
      )
  
      pad_widths = [
          (
              (weight.shape[i] - 1) * dilation[i]
              + max([output_padding[i] - padding[i], 0]),
          )
          * 2
          for i in range(dims)
      ]
  
      ret = ivy.conv_general_dilated(
          input,
          weight,
          1,
          pad_widths,
          dims=dims,
          data_format="channel_first",
          feature_group_count=groups,
          x_dilations=stride,
          dilations=dilation,
          bias=bias,
      )
      unpad_slice = (slice(None),) * 2
      for i in range(dims):
          unpad_slice += (
              slice(
                  max(
                      [
                          padding[i] - (dilation[i] // 2),
                          padding[i],
                          output_padding[i],
                      ]
                  ),
                  ret.shape[2 + i]
                  - padding[i]
                  + output_padding[i]
                  + (dilation[i] // 2),
                  1,
              ),
          )
      ret = ret[unpad_slice]
      return ret
  
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 2 for calling a global function with example usage: _conv_transpose(input, weight, bias, stride, padding, output_padding, groups, dilation) and returns: ret