code: def conv_general_transpose(
      x: JaxArray,
      filters: JaxArray,
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: str,
      /,
      *,
      dims: int = 2,
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      filter_format: str = "channel_last",
      data_format: str = "channel_last",
      dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      feature_group_count: Optional[int] = 1,
      bias: Optional[JaxArray] = None,
      out: Optional[JaxArray] = None,
  ):
      strides = (
          [strides] * dims if isinstance(strides, int) else strides
      )
      dilations = (
          [dilations] * dims
          if isinstance(dilations, int)
          else dilations
      )
      if filter_format == "channel_first":
          filters = jnp.transpose(filters, (*range(2, dims + 2), 1, 0))
      df = _get_x_data_format(dims, "channel_last")
      filter_df = _get_filter_dataformat(dims)
      if data_format == "channel_first":
          x = jnp.transpose(x, (0, *range(2, dims + 2), 1))
      padding = _get_tranpose_padding(
          x.shape[1:],
          filters.shape,
          strides,
          padding,
          dims,
          dilations,
          output_shape,
      )
      res = jnp.concatenate(
          [
              jlax.conv_transpose(
                  x[
                      ...,
                      j : j + filters.shape[-1] // feature_group_count,
                  ],
                  filters[
                      ...,
                      j : j + filters.shape[-1] // feature_group_count,
                  ],
                  strides,
                  padding,
                  dilations,
                  (df, filter_df, df),
                  True,
              )
              for j in range(
                  0,
                  filters.shape[-1],
                  filters.shape[-1] // feature_group_count,
              )
          ],
          axis=-1,
      )
      res = jnp.add(res, bias) if bias is not None else res
      if data_format == "channel_first":
          return jnp.transpose(res, (0, dims + 1, *range(1, dims + 1)))
      return res
  
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 13 for calling a global function with example usage: conv_general_transpose() and returns: res, jnp