code: def test_conv_transpose(
      self,
      use_cpu_only,
      backend,
      op,
      padding,
      data_format,
      spatial_dim_and_ks,
      output_padding,
      strides,
      dilations,
      batch_size,
  ):
      s1, s2, s3, k1, k2, k3 = spatial_dim_and_ks
      c_in, c_out = 2, 3
      input_shape = None
      kernel_size = None
      if op == tf.keras.layers.Conv2DTranspose:
          input_shape = (batch_size, s2, s3, c_in)
          kernel_size = (k2, k3)
          strides = (strides[1], strides[2])
          dilations = dilations[1:]
          output_padding = (output_padding[1], output_padding[2])
      elif op == tf.keras.layers.Conv3DTranspose:
          input_shape = (batch_size, s1, s2, s3, c_in)
          kernel_size = (k1, k2, k3)
  
      model = tf.keras.Sequential(
          [
              op(
                  batch_input_shape=input_shape,
                  filters=c_out,
                  kernel_size=kernel_size,
                  strides=strides,
                  padding=padding.upper(),
                  output_padding=output_padding,
                  data_format=data_format,
                  dilation_rate=dilations,
              )
          ]
      )
  
      run_compare_tf_keras(
          model,
          [random_gen(input_shape, rand_min=-10, rand_max=10)],
          use_cpu_only=use_cpu_only,
          backend=backend,
      )
  
imports: import random
  from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import run_compare_tf_keras
  from coremltools.converters.mil.testing_reqs import *
label_desc: Use image node_id 2 for calling the TestConvolution obj's underlying member method code with example usage: obj.test_conv_transpose(use_cpu_only, backend, op, padding, data_format, spatial_dim_and_ks, output_padding, strides, dilations, batch_size) without return types