code: def conv_transpose1d(
      input,
      weight,
      bias=None,
      stride=1,
      padding=0,
      output_padding=0,
      groups=1,
      dilation=1,
  ):
      if ivy.current_backend_str() in ["torch"]:
          # this backend supports explicit padding, no need for conv_general_dilated
          return ivy.conv_general_transpose(
              input,
              weight,
              stride,
              _get_transpose_pad(padding, output_padding, 1),
              dims=1,
              filter_format="channel_first",
              data_format="channel_first",
              dilations=dilation,
              feature_group_count=groups,
              bias=bias,
          )
      else:
          return _conv_transpose(
              input,
              weight,
              bias=bias,
              stride=stride,
              padding=padding,
              output_padding=output_padding,
              groups=groups,
              dilation=dilation,
          )
  
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 7 for calling a global function with example usage: conv_transpose1d(input, weight, bias, stride, padding, output_padding, groups, dilation) and returns: ivy, _conv_transpose