class_docstr: Benchmark NCF model using real data.
code: def __init__(
      self,
      output_dir=None,
      root_data_dir=None,
      default_flags=None,
      **kwargs,
  ):
      root_data_dir = root_data_dir if root_data_dir else ""
      default_flags = {}
      default_flags["dataset"] = "ml-20m"
      default_flags["num_gpus"] = 1
      default_flags["train_epochs"] = 10
      default_flags["clean"] = True
      default_flags["batch_size"] = 99000
      default_flags["learning_rate"] = 0.00382059
      default_flags["beta1"] = 0.783529
      default_flags["beta2"] = 0.909003
      default_flags["epsilon"] = 1.45439e-07
      default_flags["layers"] = [256, 256, 128, 64]
      default_flags["num_factors"] = 64
      default_flags["hr_threshold"] = 0.635
      default_flags["ml_perf"] = True
      default_flags["use_synthetic_data"] = False
      default_flags["data_dir"] = os.path.join(
          root_data_dir, NCF_DATA_DIR_NAME
      )
  
      super(NCFKerasAccuracy, self).__init__(
          output_dir=output_dir, default_flags=default_flags, **kwargs
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import time
  from absl import flags
  from absl.testing import flagsaver
  import tensorflow
  from utils import benchmark_wrappers
  from utils.recommendation import ncf_common
  from utils.recommendation import ncf_keras_main
  from utils.flags import core
label_desc: Use image node_id 1 to create a new NCFKerasAccuracy object from inherited base classes: NCFKerasBenchmarkBase with example: obj = NCFKerasAccuracy(output_dir, root_data_dir, default_flags)