code: def _dummy_data(batch_size, num_vertices, num_channels):
      """Create inputs for feature_steered_convolution."""
      if batch_size > 0:
          data = np.zeros(
              shape=(batch_size, num_vertices, num_channels),
              dtype=np.float32,
          )
          neighbors = _dense_to_sparse(
              np.tile(
                  np.eye(num_vertices, dtype=np.float32),
                  (batch_size, 1, 1),
              )
          )
      else:
          data = np.zeros(
              shape=(num_vertices, num_channels), dtype=np.float32
          )
          neighbors = _dense_to_sparse(
              np.eye(num_vertices, dtype=np.float32)
          )
      return data, neighbors
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  from six.moves import range
  from six.moves import zip
  import tensorflow
  import tensorflow_graphics.nn.layer.graph_convolution
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 2 for calling a global function with example usage: _dummy_data(batch_size, num_vertices, num_channels) and returns: data,  neighbors