code: def build_graph(
      embedding_files,
      output_graph_path,
      similarity_threshold=0.8,
      id_feature_name="id",
      embedding_feature_name="embedding",
      lsh_splits=0,
      lsh_rounds=2,
      random_seed=None,
  ):
      """Like `nsl.tools.build_graph_from_config`, but with individual parameters.
  
      This API exists to maintain backward compatibility, but is deprecated in favor
      of using `nsl.tools.build_graph_from_config` instead.
  
      Args:
        embedding_files: A list of names of TFRecord files containing
          `tf.train.Example` objects, which in turn contain dense embeddings.
        output_graph_path: Name of the file to which the output graph in TSV format
          should be written.
        similarity_threshold: Threshold used to determine which edges to retain in
          the resulting graph.
        id_feature_name: The name of the feature in the input `tf.train.Example`
          objects representing the ID of examples.
        embedding_feature_name: The name of the feature in the input
          `tf.train.Example` objects representing the embedding of examples.
        lsh_splits: Determines the maximum number of LSH buckets into which input
          data points will be bucketed by the graph builder. See the
          `nsl.tools.build_graph_from_config` documentation for details.
        lsh_rounds: The number of rounds of LSH bucketing to perform when
          `lsh_splits > 0`. This is also the number of LSH buckets each point will
          be hashed into.
        random_seed: Value used to seed the random number generator used to perform
          randomized LSH bucketing of the inputs when `lsh_splits > 0`. By default,
          the generator will be initialized randomly, but setting this to any
          integer will initialize it deterministically.
  
      Raises:
          ValueError: If `lsh_splits < 0` or if `lsh_splits > 0 and lsh_rounds < 1`.
      """
      build_graph_from_config(
          embedding_files,
          output_graph_path,
          nsl_configs.GraphBuilderConfig(
              id_feature_name=id_feature_name,
              embedding_feature_name=embedding_feature_name,
              similarity_threshold=similarity_threshold,
              lsh_splits=lsh_splits,
              lsh_rounds=lsh_rounds,
              random_seed=random_seed,
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import time
  from absl import app
  from absl import flags
  from absl import logging
  import neural_structured_learning.configs
  import numpy
  import six
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: build_graph(embedding_files, output_graph_path, similarity_threshold, id_feature_name, embedding_feature_name, lsh_splits, lsh_rounds, random_seed) without return types