code: def _get_sparse_feature(
      nodes_or_edges, feature_names, op, thread_num, default_values=None
  ):
      feature_names = map(str, feature_names)
      if not hasattr(feature_names, "__len__"):
          feature_names = list(feature_names)
  
      if default_values is None:
          default_values = [0] * len(feature_names)
  
      split_data_list = _split_input_data(nodes_or_edges, thread_num)
      split_result_list = [
          op(
              split_data,
              feature_names,
              default_values,
              len(feature_names),
          )
          for split_data in split_data_list
      ]
      split_sp = []
      for i in range(len(split_result_list)):
          split_sp.append(
              [
                  tf.SparseTensor(*sp)
                  for sp in zip(*split_result_list[i])
              ]
          )
      split_sp_transpose = map(list, zip(*split_sp))
      return [
          tf.sparse_concat(
              axis=0, sp_inputs=sp, expand_nonconcat_dim=True
          )
          for sp in split_sp_transpose
      ]
  
file_path: euler/tf_euler/python/euler_ops/feature_ops.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_euler.python.euler_ops import base
label_desc: Use image node_id 2 for calling a global function with example usage: _get_sparse_feature(nodes_or_edges, feature_names, op, thread_num, default_values) and returns: unknown