code: def conv_general_transpose(
      x: Union[tf.Tensor, tf.Variable],
      filters: Union[tf.Tensor, tf.Variable],
      strides: Union[int, Tuple[int, int]],
      padding: str,
      /,
      *,
      dims: int = 2,
      filter_format: str = "channel_last",
      data_format: str = "channel_last",
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      feature_group_count: int = 1,
      bias: Optional[Union[tf.Tensor, tf.Variable]] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if feature_group_count == 1:
          if dims == 1:
              return conv1d_transpose(
                  x,
                  filters,
                  strides,
                  padding,
                  output_shape=output_shape,
                  filter_format=filter_format,
                  data_format="NWC"
                  if data_format == "channel_last"
                  else "NCW",
                  dilations=dilations,
                  bias=bias,
              )
          elif dims == 2:
              return conv2d_transpose(
                  x,
                  filters,
                  strides,
                  padding,
                  output_shape=output_shape,
                  filter_format=filter_format,
                  data_format="NHWC"
                  if data_format == "channel_last"
                  else "NCHW",
                  dilations=dilations,
                  bias=bias,
              )
          else:
              return conv3d_transpose(
                  x,
                  filters,
                  strides,
                  padding,
                  output_shape=output_shape,
                  filter_format=filter_format,
                  data_format="NDHWC"
                  if data_format == "channel_last"
                  else "NCDHW",
                  dilations=dilations,
                  bias=bias,
              )
      else:
          if filter_format == "channel_first":
              filters = tf.transpose(
                  filters, (*range(2, dims + 2), 1, 0)
              )
          permuted_x = False
          if data_format == "channel_first" and ivy.dev(x) == "cpu":
              x = tf.transpose(x, (0, *range(2, dims + 2), 1))
              data_format = "channel_last"
              permuted_x = True
  
          data_format = _get_x_data_format(dims, data_format)
  
          output_shape, padding = _transpose_out_pad(
              x.shape,
              filters.shape,
              strides,
              padding,
              dims,
              dilations,
              data_format,
          )
  
          if dims == 1:
              res = tf.concat(
                  [
                      tf.nn.conv1d_transpose(
                          x[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                          ],
                          filters[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                              :,
                          ],
                          output_shape,
                          strides,
                          padding=padding,
                          data_format=data_format,
                          dilations=dilations,
                      )
                      for j in range(
                          0,
                          filters.shape[-2],
                          filters.shape[-2] // feature_group_count,
                      )
                  ],
                  axis=-1,
              )
          elif dims == 2:
              res = tf.concat(
                  [
                      tf.nn.conv2d_transpose(
                          x[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                          ],
                          filters[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                              :,
                          ],
                          output_shape,
                          strides,
                          padding=padding,
                          data_format=data_format,
                          dilations=dilations,
                      )
                      for j in range(
                          0,
                          filters.shape[-2],
                          filters.shape[-2] // feature_group_count,
                      )
                  ],
                  axis=-1,
              )
          else:
              strides, dilations = _extend_3d_strides_dilations(
                  strides, dilations, data_format
              )
              res = tf.concat(
                  [
                      tf.nn.conv3d_transpose(
                          x[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                          ],
                          filters[
                              ...,
                              j : j
                              + filters.shape[-2]
                              // feature_group_count,
                              :,
                          ],
                          output_shape,
                          strides,
                          padding=padding,
                          data_format=data_format,
                          dilations=dilations,
                      )
                      for j in range(
                          0,
                          filters.shape[-2],
                          filters.shape[-2] // feature_group_count,
                      )
                  ],
                  axis=-1,
              )
  
          res = tf.math.add(res, bias) if bias is not None else res
          if permuted_x:
              return tf.transpose(
                  res, (0, dims + 1, *range(1, dims + 1))
              )
          return res
  
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 15 for calling a global function with example usage: conv_general_transpose() and returns: res, conv1d_transpose, tf, conv2d_transpose, conv3d_transpose