code: def define_ncf_flags():
      """Add flags for running ncf_main."""
      # Add common flags
      flags_core.define_base(
          model_dir=True,
          clean=True,
          train_epochs=True,
          epochs_between_evals=True,
          export_dir=False,
          run_eagerly=True,
          stop_threshold=True,
          num_gpu=True,
          hooks=True,
          distribution_strategy=True,
      )
      flags_core.define_performance(
          synthetic_data=True,
          dtype=True,
          fp16_implementation=True,
          loss_scale=True,
          dynamic_loss_scale=True,
          enable_xla=True,
          force_v2_in_keras_compile=True,
      )
      flags_core.define_device(tpu=True)
      flags_core.define_benchmark()
  
      flags.adopt_module_key_flags(flags_core)
  
      flags_core.set_defaults(
          model_dir="/tmp/ncf/",
          data_dir="/tmp/movielens-data/",
          train_epochs=2,
          batch_size=256,
          hooks="ProfilerHook",
          tpu=None,
      )
  
      # Add ncf-specific flags
      flags.DEFINE_enum(
          name="dataset",
          default="ml-1m",
          enum_values=["ml-1m", "ml-20m", "ml-20mx16x32"],
          case_sensitive=False,
          help=flags_core.help_wrap(
              "Dataset to be trained and evaluated."
          ),
      )
  
      flags.DEFINE_boolean(
          name="download_if_missing",
          default=True,
          help=flags_core.help_wrap(
              "Download data to data_dir if it is not already present."
          ),
      )
  
      flags.DEFINE_integer(
          name="eval_batch_size",
          default=None,
          help=flags_core.help_wrap(
              "The batch size used for evaluation. This should generally be larger"
              "than the training batch size as the lack of back propagation during"
              "evaluation can allow for larger batch sizes to fit in memory. If not"
              "specified, the training batch size (--batch_size) will be used."
          ),
      )
  
      flags.DEFINE_integer(
          name="num_factors",
          default=8,
          help=flags_core.help_wrap("The Embedding size of MF model."),
      )
  
      # Set the default as a list of strings to be consistent with input
      # arguments
      flags.DEFINE_list(
          name="layers",
          default=["64", "32", "16", "8"],
          help=flags_core.help_wrap(
              "The sizes of hidden layers for MLP. Example "
              "to specify different sizes of MLP layers: --layers=32,16,8,4"
          ),
      )
  
      flags.DEFINE_float(
          name="mf_regularization",
          default=0.0,
          help=flags_core.help_wrap(
              "The regularization factor for MF embeddings. The factor is used by "
              "regularizer which allows to apply penalties on layer parameters or "
              "layer activity during optimization."
          ),
      )
  
      flags.DEFINE_list(
          name="mlp_regularization",
          default=["0.", "0.", "0.", "0."],
          help=flags_core.help_wrap(
              "The regularization factor for each MLP layer. See mf_regularization "
              "help for more info about regularization factor."
          ),
      )
  
      flags.DEFINE_integer(
          name="num_neg",
          default=4,
          help=flags_core.help_wrap(
              "The Number of negative instances to pair with a positive instance."
          ),
      )
  
      flags.DEFINE_float(
          name="learning_rate",
          default=0.001,
          help=flags_core.help_wrap("The learning rate."),
      )
  
      flags.DEFINE_float(
          name="beta1",
          default=0.9,
          help=flags_core.help_wrap(
              "beta1 hyperparameter for the Adam optimizer."
          ),
      )
  
      flags.DEFINE_float(
          name="beta2",
          default=0.999,
          help=flags_core.help_wrap(
              "beta2 hyperparameter for the Adam optimizer."
          ),
      )
  
      flags.DEFINE_float(
          name="epsilon",
          default=1e-8,
          help=flags_core.help_wrap(
              "epsilon hyperparameter for the Adam " "optimizer."
          ),
      )
  
      flags.DEFINE_float(
          name="hr_threshold",
          default=1.0,
          help=flags_core.help_wrap(
              "If passed, training will stop when the evaluation metric HR is "
              "greater than or equal to hr_threshold. For dataset ml-1m, the "
              "desired hr_threshold is 0.68 which is the result from the paper; "
              "For dataset ml-20m, the threshold can be set as 0.95 which is "
              "achieved by MLPerf implementation."
          ),
      )
  
      flags.DEFINE_enum(
          name="constructor_type",
          default="bisection",
          enum_values=["bisection", "materialized"],
          case_sensitive=False,
          help=flags_core.help_wrap(
              "Strategy to use for generating false negatives. materialized has a"
              "precompute that scales badly, but a faster per-epoch construction"
              "time and can be faster on very large systems."
          ),
      )
  
      flags.DEFINE_string(
          name="train_dataset_path",
          default=None,
          help=flags_core.help_wrap("Path to training data."),
      )
  
      flags.DEFINE_string(
          name="eval_dataset_path",
          default=None,
          help=flags_core.help_wrap("Path to evaluation data."),
      )
  
      flags.DEFINE_string(
          name="input_meta_data_path",
          default=None,
          help=flags_core.help_wrap("Path to input meta data file."),
      )
  
      flags.DEFINE_bool(
          name="ml_perf",
          default=False,
          help=flags_core.help_wrap(
              "If set, changes the behavior of the model slightly to match the "
              "MLPerf reference implementations here: \n"
              "https://github.com/mlperf/reference/tree/master/recommendation/"
              "pytorch\n"
              "The two changes are:\n"
              "1. When computing the HR and NDCG during evaluation, remove "
              "duplicate user-item pairs before the computation. This results in "
              "better HRs and NDCGs.\n"
              "2. Use a different soring algorithm when sorting the input data, "
              "which performs better due to the fact the sorting algorithms are "
              "not stable."
          ),
      )
  
      flags.DEFINE_bool(
          name="output_ml_perf_compliance_logging",
          default=False,
          help=flags_core.help_wrap(
              "If set, output the MLPerf compliance logging. This is only useful "
              "if one is running the model for MLPerf. See "
              "https://github.com/mlperf/policies/blob/master/training_rules.adoc"
              "#submission-compliance-logs for details. This uses sudo and so may "
              "ask for your password, as root access is needed to clear the system "
              "caches, which is required for MLPerf compliance."
          ),
      )
  
      flags.DEFINE_integer(
          name="seed",
          default=None,
          help=flags_core.help_wrap(
              "This value will be used to seed both NumPy and TensorFlow."
          ),
      )
  
      @flags.validator(
          "eval_batch_size",
          "eval_batch_size must be at least {}".format(
              rconst.NUM_EVAL_NEGATIVES + 1
          ),
      )
      def eval_size_check(eval_batch_size):
          return (
              eval_batch_size is None
              or int(eval_batch_size) > rconst.NUM_EVAL_NEGATIVES
          )
  
      flags.DEFINE_bool(
          name="use_xla_for_gpu",
          default=False,
          help=flags_core.help_wrap(
              "If True, use XLA for the model function. Only works when using a "
              "GPU. On TPUs, XLA is always used"
          ),
      )
  
      xla_message = "--use_xla_for_gpu is incompatible with --tpu"
  
      @flags.multi_flags_validator(
          ["use_xla_for_gpu", "tpu"], message=xla_message
      )
      def xla_validator(flag_dict):
          return (
              not flag_dict["use_xla_for_gpu"] or not flag_dict["tpu"]
          )
  
      flags.DEFINE_bool(
          name="early_stopping",
          default=False,
          help=flags_core.help_wrap(
              "If True, we stop the training when it reaches hr_threshold"
          ),
      )
  
      flags.DEFINE_bool(
          name="keras_use_ctl",
          default=False,
          help=flags_core.help_wrap(
              "If True, we use a custom training loop for keras."
          ),
      )
  
file_path: autodist/examples/benchmark/utils/recommendation/ncf_common.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import numpy
  from absl import flags
  from absl import logging
  import tensorflow
  from utils.recommendation import constants
  from utils.recommendation import data_pipeline
  from utils.recommendation import data_preprocessing
  from utils.recommendation import movielens
  from utils.flags import core
  from utils.misc import keras_utils
label_desc: Use image node_id 3 for calling a global function with example usage: define_ncf_flags() and returns: unknown, unknown