docstr: Test the jacobian is correct for preset inputs.
code: def test_feature_steered_convolution_jacobian_preset(
      self, num_vertices, num_channels, data_multiplier
  ):
      """Test the jacobian is correct for preset inputs."""
      # Corner cases include one vertex, one channel, and all-zero features.
      data_init = data_multiplier * np.random.uniform(
          size=(num_vertices, num_channels)
      ).astype(np.float64)
      neighbors = tf.sparse.eye(num_vertices, dtype=tf.float64)
      u, v, c, w, b = _random_variables(
          num_channels, num_channels, 1, dtype=np.float64
      )
  
      def feature_steered_convolution(data):
          return gc.feature_steered_convolution(
              data=data,
              neighbors=neighbors,
              sizes=None,
              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 9 for calling the GraphConvolutionTestFeatureSteeredConvolutionTests obj's underlying member method code with example usage: obj.test_feature_steered_convolution_jacobian_preset(num_vertices, num_channels, data_multiplier) and returns: gc