code: def np_conv1d_transpose(
      x,
      kernel_weights,
      bias_weights,
      strides,
      padding,
      output_padding,
      data_format,
      dilation_rate,
  ):
      if data_format == "channels_first":
          x = x.transpose((0, 2, 1))
      if isinstance(strides, (tuple, list)):
          h_stride = strides[0]
      else:
          h_stride = strides
      if isinstance(dilation_rate, (tuple, list)):
          h_dilation = dilation_rate[0]
      else:
          h_dilation = dilation_rate
  
      h_kernel, ch_out, ch_in = kernel_weights.shape
      n_batch, h_x, _ = x.shape
      # Get output shape and padding
      _, h_out, _ = compute_conv_transpose_output_shape(
          x.shape,
          kernel_weights.shape,
          ch_out,
          strides,
          padding,
          output_padding,
          "channels_last",
          dilation_rate,
      )
      jax_padding = compute_conv_transpose_padding_args_for_jax(
          input_shape=x.shape,
          kernel_shape=kernel_weights.shape,
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          dilation_rate=dilation_rate,
      )
      h_pad_side1 = h_kernel - 1 - jax_padding[0][0]
  
      if h_dilation > 1:
          # Increase kernel size
          new_h_kernel = h_kernel + (h_dilation - 1) * (h_kernel - 1)
          new_kenel_size_tuple = (new_h_kernel,)
          new_kernel_weights = np.zeros(
              (*new_kenel_size_tuple, ch_out, ch_in),
              dtype=kernel_weights.dtype,
          )
          new_kernel_weights[::h_dilation] = kernel_weights
          kernel_weights = new_kernel_weights
          h_kernel = kernel_weights.shape[0]
  
      # Compute output
      output = np.zeros([n_batch, h_out + h_kernel, ch_out])
      for nb in range(n_batch):
          for h_x_idx in range(h_x):
              h_out_idx = h_x_idx * h_stride  # Index in output
              output[nb, h_out_idx : h_out_idx + h_kernel, :] += np.sum(
                  kernel_weights[:, :, :] * x[nb, h_x_idx, :], axis=-1
              )
      output = output + bias_weights
  
      # Cut padding results from output
      output = output[:, h_pad_side1 : h_out + h_pad_side1]
      if data_format == "channels_first":
          output = output.transpose((0, 2, 1))
      return output
  
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from keras import backend
  from keras import layers
  from keras import testing
  from keras.backend.common.backend_utils import _convert_conv_tranpose_padding_args_from_keras_to_torch
  from keras.backend.common.backend_utils import compute_conv_transpose_output_shape
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
label_desc: Use image node_id 1 for calling a global function with example usage: np_conv1d_transpose(x, kernel_weights, bias_weights, strides, padding, output_padding, data_format, dilation_rate) and returns: output