text: import argparse
  import json
  import os
  
  from . import model
  
  
  def convert_string_to_bool(string):
      """Converts string to bool.
      Args:
          string: str, string to convert.
      Returns:
          Boolean conversion of string.
      """
      return False if string.lower() == "false" else True
  
  
  def convert_string_to_none_or_float(string):
      """Converts string to None or float.
  
      Args:
          string: str, string to convert.
  
      Returns:
          None or float conversion of string.
      """
      return None if string.lower() == "none" else float(string)
  
  
  def convert_string_to_none_or_int(string):
      """Converts string to None or int.
  
      Args:
          string: str, string to convert.
  
      Returns:
          None or int conversion of string.
      """
      return None if string.lower() == "none" else int(string)
  
  
  def convert_string_to_list_of_ints(string, sep):
      """Converts string to list of ints.
  
      Args:
          string: str, string to convert.
          sep: str, separator string.
  
      Returns:
          List of ints conversion of string.
      """
      return [int(x) for x in string.split(sep)]
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      # File arguments.
      parser.add_argument(
          "--train_file_pattern",
          help="GCS location to read training data.",
          required=True
      )
      parser.add_argument(
          "--eval_file_pattern",
          help="GCS location to read evaluation data.",
          required=True
      )
      parser.add_argument(
          "--output_dir",
          help="GCS location to write checkpoints and export models.",
          required=True
      )
      parser.add_argument(
          "--job-dir",
          help="This model ignores this field, but it is required by gcloud.",
          default="junk"
      )
  
      # Training parameters.
      parser.add_argument(
          "--tf_version",
          help="Version of TensorFlow",
          type=float,
          default=2.2
      )
      parser.add_argument(
          "--use_graph_mode",
          help="Whether to use graph mode or not (eager).",
          type=str,
          default="True"
      )
      parser.add_argument(
          "--distribution_strategy",
          help="Which distribution strategy to use, if any.",
          type=str,
          default=""
      )
      parser.add_argument(
          "--write_summaries",
          help="Whether to write summaries for TensorBoard.",
          type=str,
          default="True"
      )
      parser.add_argument(
          "--num_epochs",
          help="Number of epochs to train for.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--train_dataset_length",
          help="Number of examples in one epoch of training set",
          type=int,
          default=100
      )
      parser.add_argument(
          "--train_batch_size",
          help="Number of examples in training batch.",
          type=int,
          default=32
      )
      parser.add_argument(
          "--log_step_count_steps",
          help="How many steps to train before writing steps and loss to log.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--save_summary_steps",
          help="How many steps to train before saving a summary.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--save_checkpoints_steps",
          help="How many steps to train before saving a checkpoint.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--keep_checkpoint_max",
          help="Max number of checkpoints to keep.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--input_fn_autotune",
          help="Whether to autotune input function performance.",
          type=str,
          default="True"
      )
  
      # Eval parameters.
      parser.add_argument(
          "--eval_batch_size",
          help="Number of examples in evaluation batch.",
          type=int,
          default=32
      )
      parser.add_argument(
          "--eval_steps",
          help="Number of steps to evaluate for.",
          type=str,
          default="None"
      )
  
      # Image parameters.
      parser.add_argument(
          "--height",
          help="Height of image.",
          type=int,
          default=32
      )
      parser.add_argument(
          "--width",
          help="Width of image.",
          type=int,
          default=32
      )
      parser.add_argument(
          "--depth",
          help="Depth of image.",
          type=int,
          default=3
      )
  
      # Generator parameters.
      parser.add_argument(
          "--latent_size",
          help="The latent size of the noise vector.",
          type=int,
          default=3
      )
      parser.add_argument(
          "--generator_hidden_units",
          help="Hidden layer sizes to use for generator.",
          type=str,
          default="2,4,8"
      )
      parser.add_argument(
          "--generator_leaky_relu_alpha",
          help="The amount of leakyness of generator's leaky relus.",
          type=float,
          default=0.2
      )
      parser.add_argument(
          "--generator_final_activation",
          help="The final activation function of generator.",
          type=str,
          default="None"
      )
      parser.add_argument(
          "--generator_l1_regularization_scale",
          help="Scale factor for L1 regularization for generator.",
          type=float,
          default=0.0
      )
      parser.add_argument(
          "--generator_l2_regularization_scale",
          help="Scale factor for L2 regularization for generator.",
          type=float,
          default=0.0
      )
      parser.add_argument(
          "--generator_optimizer",
          help="Name of optimizer to use for generator.",
          type=str,
          default="Adam"
      )
      parser.add_argument(
          "--generator_learning_rate",
          help="How quickly we train our model by scaling the gradient for generator.",
          type=float,
          default=0.001
      )
      parser.add_argument(
          "--generator_adam_beta1",
          help="Adam optimizer's beta1 hyperparameter for first moment.",
          type=float,
          default=0.9
      )
      parser.add_argument(
          "--generator_adam_beta2",
          help="Adam optimizer's beta2 hyperparameter for second moment.",
          type=float,
          default=0.999
      )
      parser.add_argument(
          "--generator_adam_epsilon",
          help="Adam optimizer's epsilon hyperparameter for numerical stability.",
          type=float,
          default=1e-8
      )
      parser.add_argument(
          "--generator_clip_gradients",
          help="Global clipping to prevent gradient norm to exceed this value for generator.",
          type=str,
          default="None"
      )
      parser.add_argument(
          "--generator_train_steps",
          help="Number of steps to train generator for per cycle.",
          type=int,
          default=100
      )
  
      # Discriminator parameters.
      parser.add_argument(
          "--discriminator_hidden_units",
          help="Hidden layer sizes to use for discriminator.",
          type=str,
          default="2,4,8"
      )
      parser.add_argument(
          "--discriminator_leaky_relu_alpha",
          help="The amount of leakyness of discriminator's leaky relus.",
          type=float,
          default=0.2
      )
      parser.add_argument(
          "--discriminator_l1_regularization_scale",
          help="Scale factor for L1 regularization for discriminator.",
          type=float,
          default=0.0
      )
      parser.add_argument(
          "--discriminator_l2_regularization_scale",
          help="Scale factor for L2 regularization for discriminator.",
          type=float,
          default=0.0
      )
      parser.add_argument(
          "--discriminator_optimizer",
          help="Name of optimizer to use for discriminator.",
          type=str,
          default="Adam"
      )
      parser.add_argument(
          "--discriminator_learning_rate",
          help="How quickly we train our model by scaling the gradient for discriminator.",
          type=float,
          default=0.001
      )
      parser.add_argument(
          "--discriminator_adam_beta1",
          help="Adam optimizer's beta1 hyperparameter for first moment.",
          type=float,
          default=0.9
      )
      parser.add_argument(
          "--discriminator_adam_beta2",
          help="Adam optimizer's beta2 hyperparameter for second moment.",
          type=float,
          default=0.999
      )
      parser.add_argument(
          "--discriminator_adam_epsilon",
          help="Adam optimizer's epsilon hyperparameter for numerical stability.",
          type=float,
          default=1e-8
      )
      parser.add_argument(
          "--discriminator_clip_gradients",
          help="Global clipping to prevent gradient norm to exceed this value for discriminator.",
          type=str,
          default="None"
      )
      parser.add_argument(
          "--discriminator_train_steps",
          help="Number of steps to train discriminator for per cycle.",
          type=int,
          default=100
      )
      parser.add_argument(
          "--label_smoothing",
          help="Multiplier when making real labels instead of all ones.",
          type=float,
          default=0.9
      )
  
      # Parse all arguments.
      args = parser.parse_args()
      arguments = args.__dict__
  
      # Unused args provided by service.
      arguments.pop("job_dir", None)
      arguments.pop("job-dir", None)
  
      # Fix use_graph_mode.
      arguments["use_graph_mode"] = convert_string_to_bool(
          string=arguments["use_graph_mode"]
      )
  
      # Fix write_summaries.
      arguments["write_summaries"] = convert_string_to_bool(
          string=arguments["write_summaries"]
      )
  
      # Fix input_fn_autotune.
      arguments["input_fn_autotune"] = convert_string_to_bool(
          string=arguments["input_fn_autotune"]
      )
  
      # Fix eval steps.
      arguments["eval_steps"] = convert_string_to_none_or_int(
          string=arguments["eval_steps"])
  
      # Fix hidden_units.
      arguments["generator_hidden_units"] = convert_string_to_list_of_ints(
          string=arguments["generator_hidden_units"], sep=","
      )
  
      arguments["discriminator_hidden_units"] = convert_string_to_list_of_ints(
          string=arguments["discriminator_hidden_units"], sep=","
      )
  
      # Fix clip_gradients.
      arguments["generator_clip_gradients"] = convert_string_to_none_or_float(
          string=arguments["generator_clip_gradients"]
      )
  
      arguments["discriminator_clip_gradients"] = convert_string_to_none_or_float(
          string=arguments["discriminator_clip_gradients"]
      )
  
      # Append trial_id to path if we are doing hptuning.
      # This code can be removed if you are not using hyperparameter tuning.
      arguments["output_dir"] = os.path.join(
          arguments["output_dir"],
          json.loads(
              os.environ.get(
                  "TF_CONFIG", "{}"
              )
          ).get("task", {}).get("trial", ""))
  
      # Instantiate instance of model train and evaluate loop.
      train_and_evaluate_loop = model.TrainAndEvaluateLoop(params=arguments)
  
      # Run the training job.
      train_and_evaluate_loop.train_and_evaluate()
  