code: def test_conv2d_transpose(
      self,
      filters,
      kernel_size,
      strides,
      padding,
      output_padding,
      data_format,
      dilation_rate,
  ):
      layer = layers.Conv2DTranspose(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
      )
  
      inputs = np.random.normal(size=[2, 14, 14, 4])
      layer.build(input_shape=inputs.shape)
  
      kernel_shape = layer.kernel.shape
      kernel_weights = np.random.normal(size=kernel_shape)
      bias_weights = np.random.normal(size=(filters,))
      layer.kernel.assign(kernel_weights)
      layer.bias.assign(bias_weights)
  
      outputs = layer(inputs)
      expected = np_conv2d_transpose(
          inputs,
          kernel_weights,
          bias_weights,
          strides,
          padding,
          output_padding,
          data_format,
          dilation_rate,
      )
      self.assertAllClose(outputs, expected, 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 2 for calling the ConvTransposeCorrectnessTest obj's underlying member method code with example usage: obj.test_conv2d_transpose(filters, kernel_size, strides, padding, output_padding, data_format, dilation_rate) without return types