docstr: Tests the jacobian is correct with padded data.
docstr_tok: ["Tests","the","jacobian","is","correct","with","padded","data","."]
code: def test_upsample_transposed_convolution_jacobian_random_padding(
      self,
  ):
      """Tests the jacobian is correct with padded data."""
      num_filters = 6
      sizes = ((2, 4), (3, 5))
      data_init = np.random.uniform(size=(2, 3, num_filters))
      data_init[0, -1, :] = 0.0
      pool_map = np.array(
          (
              (
                  (0.5, 0.5, 0.0, 0.0, 0.0),
                  (0.0, 0.0, 0.5, 0.5, 0.0),
                  (0.0, 0.0, 0.0, 0.0, 0.0),
              ),
              (
                  (1.0, 0.0, 0.0, 0.0, 0.0),
                  (0.0, 1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0, 0.0),
                  (0.0, 0.0, 0.0, 0.0, 1.0),
              ),
          ),
          dtype=data_init.dtype,
      )
      pool_map = _dense_to_sparse(pool_map)
      kernel_size = 2
      transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
          filters=num_filters,
          kernel_size=(1, kernel_size),
          strides=(1, kernel_size),
          padding="valid",
          dtype="float64",
      )
  
      # Calling the upsample_transposed_convolution to create the variables
      # in the transposed_convoution.
      gp.upsample_transposed_convolution(
          data_init,
          pool_map,
          sizes=sizes,
          kernel_size=kernel_size,
          transposed_convolution_op=transposed_convolution_op,
      )
  
      def gp_upsample_transposed_convolution(data):
          return gp.upsample_transposed_convolution(
              data,
              pool_map,
              sizes=sizes,
              kernel_size=kernel_size,
              transposed_convolution_op=transposed_convolution_op,
          )
  
      # Initializes variables of the transpose conv layer.
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      self.assert_jacobian_is_correct_fn(
          gp_upsample_transposed_convolution, [data_init]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from absl.testing import parameterized
  import numpy
  from six.moves import range
  import tensorflow
  import tensorflow_graphics.geometry.convolution.graph_pooling
  from tensorflow_graphics.geometry.convolution.tests import utils_test
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 8 for calling the GraphPoolingUpsampleTransposeConvolutionTests obj's underlying member method code with example usage: obj.test_upsample_transposed_convolution_jacobian_random_padding() and returns: gp