docstr: Test the jacobian for random input data.
code: def test_feature_steered_convolution_jacobian_random(
      self,
      batch_size,
      num_vertices,
      in_channels,
      num_weight_matrices,
      padding,
  ):
      """Test the jacobian for random input data."""
      random_data = _random_data(
          batch_size,
          num_vertices,
          in_channels,
          padding,
          only_self_edges=False,
          data_type=np.float64,
          neighbors_type=np.float64,
      )
      data_init = random_data[0]
      neighbors = random_data[1]
      sizes = None if not padding else random_data[2]
      u, v, c, w, b = _random_variables(
          in_channels,
          in_channels,
          num_weight_matrices,
          dtype=np.float64,
      )
  
      def feature_steered_convolution(data):
          return gc.feature_steered_convolution(
              data=data,
              neighbors=neighbors,
              sizes=sizes,
              var_u=u,
              var_v=v,
              var_c=c,
              var_w=w,
              var_b=b,
          )
  
      self.assert_jacobian_is_correct_fn(
          feature_steered_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_convolution
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 8 for calling the GraphConvolutionTestFeatureSteeredConvolutionTests obj's underlying member method code with example usage: obj.test_feature_steered_convolution_jacobian_random(batch_size, num_vertices, in_channels, num_weight_matrices, padding) and returns: gc