code: def conv1d_transpose(
      x: np.ndarray,
      filters: np.ndarray,
      strides: Union[int, Tuple[int]],
      padding: str,
      /,
      *,
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      filter_format: str = "channel_last",
      data_format: str = "NWC",
      dilations: Union[int, Tuple[int]] = 1,
      bias: Optional[np.ndarray] = None,
      out: Optional[np.ndarray] = None,
  ) -> np.ndarray:
      if data_format == "NCW":
          x = np.transpose(x, (0, 2, 1))
      if filter_format == "channel_last":
          filters = np.transpose(filters, (0, 2, 1))
      else:
          filters = np.transpose(filters, (2, 0, 1))
      x, filters = _dilate_pad_conv_tranpose(
          x, filters, strides, padding, 1, dilations, output_shape
      )
      x = np.flip(x, (1,))
      res = np.flip(
          conv1d(
              x, filters, 1, "VALID", data_format="NWC", dilations=1
          ),
          (1,),
      )
      res = np.add(res, bias) if bias is not None else res
      if data_format == "NCW":
          res = np.transpose(res, (0, 2, 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 6 for calling a global function with example usage: conv1d_transpose() and returns: res