code: def test_node_count(
      self,
      num_non_packed_analyzers,
      num_packed_analyzers,
      num_input_datasets,
      expected_node_count_with_cache,
      expected_node_count_without_cache,
  ):
      dataset_keys = [
          analyzer_cache.DatasetKey(str(x))
          for x in range(num_input_datasets)
      ]
      specs = impl_helper.get_type_specs_from_feature_specs(
          {"x": tf.io.FixedLenFeature([], tf.int64)}
      )
  
      def preprocessing_fn(inputs):
          for _ in range(num_packed_analyzers):
              tft.mean(inputs["x"])
          for _ in range(num_non_packed_analyzers):
              tft.vocabulary(inputs["x"])
          return inputs
  
      def get_graph_leaf_nodes(cache_enabled):
          (
              graph,
              structured_inputs,
              structured_outputs,
          ) = impl_helper.trace_preprocessing_function(
              preprocessing_fn,
              specs,
              use_tf_compat_v1=False,
              base_temp_dir=self.base_test_dir,
          )
          (
              transform_fn_future,
              cache_output_dict,
              sideeffects,
          ) = analysis_graph_builder.build(
              graph,
              structured_inputs,
              structured_outputs,
              dataset_keys,
              cache_dict={} if cache_enabled else None,
          )
          cache_output_nodes = []
          if cache_output_dict:
              for cache in cache_output_dict.values():
                  cache_output_nodes.extend(cache.values())
          return (
              [transform_fn_future]
              + cache_output_nodes
              + list(sideeffects)
          )
  
      with_cache_graph = get_graph_leaf_nodes(True)
      without_cache_graph = get_graph_leaf_nodes(False)
      node_count_with_cache = nodes.count_graph_nodes(with_cache_graph)
      node_count_without_cache = nodes.count_graph_nodes(
          without_cache_graph
      )
      self._publish_rendered_dot_graph_file_from_leaf_nodes(
          without_cache_graph
      )
      self._publish_rendered_dot_graph_file_from_leaf_nodes(
          with_cache_graph
      )
      self.assertEqual(
          (
              expected_node_count_without_cache,
              expected_node_count_with_cache,
          ),
          (node_count_without_cache, node_count_with_cache),
      )
  
imports: import functools
  import os
  import struct
  from typing import Callable, Mapping, List
  import uuid
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  import tensorflow_transform
  from tensorflow_transform import analyzer_nodes
  from tensorflow_transform import common_types
  from tensorflow_transform import impl_helper
  from tensorflow_transform import nodes
  import tensorflow_transform.beam
  from tensorflow_transform.beam import analysis_graph_builder
  from tensorflow_transform.beam import analyzer_cache
  from tensorflow_transform.beam import tft_unit
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 17 for calling the CachedImplTest obj's underlying member method code with example usage: obj.test_node_count(num_non_packed_analyzers, num_packed_analyzers, num_input_datasets, expected_node_count_with_cache, expected_node_count_without_cache) and returns: inputs, unknown