docstr: Test conv transpose, on an 1D array of size 3, against several
  convolution parameters. In particular, tests if Torch inconsistencies
  are raised.
docstr_tok: ["Test","conv","transpose",",","on","an","1D","array","of","size","3",",","against","several","convolution","parameters",".","In","particular",",","tests","if","Torch","inconsistencies","are","raised","."]
code: def test_conv1d_transpose_consistency(
      self, kernel_size, strides, padding, output_padding
  ):
      """Test conv transpose, on an 1D array of size 3, against several
      convolution parameters. In particular, tests if Torch inconsistencies
      are raised.
      """
  
      # output_padding cannot be greater than strides
      if isinstance(output_padding, int) and output_padding >= strides:
          pytest.skip(
              "`output_padding` greater than `strides` is not supported"
          )
  
      if backend.config.image_data_format() == "channels_last":
          input_shape = (1, 3, 1)
      else:
          input_shape = (1, 1, 3)
  
      input = np.ones(shape=input_shape)
      kernel_weights = np.arange(1, kernel_size + 1).reshape(
          (kernel_size, 1, 1)
      )
  
      # Exepected result
      expected_res = np_conv1d_transpose(
          x=input,
          kernel_weights=kernel_weights,
          bias_weights=np.zeros(shape=(1,)),
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          data_format=backend.config.image_data_format(),
          dilation_rate=1,
      )
  
      # keras layer
      kc_layer = layers.Conv1DTranspose(
          filters=1,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          dilation_rate=1,
      )
      kc_layer.build(input_shape=input_shape)
      kc_layer.kernel.assign(kernel_weights)
  
      # Special cases for Torch
      if backend.backend() == "torch":
          # The following set of arguments lead to Torch output padding to be
          # greater than strides, which is not supported by Torch.
          # An error is raised.
          if (kernel_size, strides, padding, output_padding) in [
              (2, 1, "same", None),
              (4, 1, "same", None),
          ]:
              with pytest.raises(ValueError):
                  kc_res = kc_layer(input)
              return
  
          # When both torch_padding and torch_output_padding are greater
          # than 0, Torch outputs are inconsistent with the ones from
          # Tensorflow. A warning is raised, and we expect the results to be
          # different.
          (
              torch_padding,
              torch_output_padding,
          ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
              kernel_size=kernel_size,
              stride=strides,
              dilation_rate=1,
              padding=padding,
              output_padding=output_padding,
          )
          if torch_padding > 0 and torch_output_padding > 0:
              with pytest.raises(AssertionError):
                  kc_res = kc_layer(input)
                  self.assertAllClose(expected_res, kc_res, atol=1e-5)
              return
  
      # Compare results
      kc_res = kc_layer(input)
      self.assertAllClose(expected_res, kc_res, atol=1e-5)
  
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 4 for calling the ConvTransposeCorrectnessTest obj's underlying member method code with example usage: obj.test_conv1d_transpose_consistency(kernel_size, strides, padding, output_padding) without return types