code: def conv_general_transpose(
      x: np.ndarray,
      filters: np.ndarray,
      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: int = 1,
      bias: Optional[np.ndarray] = None,
      out: Optional[np.ndarray] = None,
  ) -> np.ndarray:
      if data_format == "channel_first":
          x = np.transpose(x, (0, *range(2, dims + 2), 1))
      if filter_format == "channel_last":
          filters = np.transpose(
              filters, (*range(dims), dims + 1, dims)
          )
      else:
          filters = np.transpose(filters, (*range(2, dims + 2), 0, 1))
  
      x, filters = _dilate_pad_conv_tranpose(
          x, filters, strides, padding, dims, dilations, output_shape
      )
  
      x = np.flip(x, (*range(1, dims + 1),))
      res = np.concatenate(
          [
              np.flip(
                  conv_general_dilated(
                      x[
                          ...,
                          j : j
                          + filters.shape[-2] // feature_group_count,
                      ],
                      filters[
                          ...,
                          j : j
                          + filters.shape[-2] // feature_group_count,
                          :,
                      ],
                      1,
                      "VALID",
                      dims=dims,
                      data_format=_get_x_data_format(
                          dims, "channel_last"
                      ),
                      dilations=1,
                  ),
                  (*range(1, dims + 1),),
              )
              for j in range(
                  0,
                  filters.shape[-2],
                  filters.shape[-2] // feature_group_count,
              )
          ],
          axis=-1,
      )
      res = np.add(res, bias) if bias is not None else res
  
      if data_format == "channel_first":
          return np.transpose(res, (0, dims + 1, *range(1, dims + 1)))
      return res
  
file_path: ivy/ivy/functional/backends/numpy/layers.py
imports: import numpy
  from typing import Union, Tuple, Optional, Sequence
  import ivy
  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, np