code: def main():
      parser = argparse.ArgumentParser(
          description="Caffe2: Char RNN Training"
      )
      parser.add_argument(
          "--train_data",
          type=str,
          default=None,
          help="Path to training data in a text file format",
          required=True,
      )
      parser.add_argument(
          "--seq_length",
          type=int,
          default=25,
          help="One training example sequence length",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="Training batch size",
      )
      parser.add_argument(
          "--iters_to_report",
          type=int,
          default=500,
          help="How often to report loss and generate text",
      )
      parser.add_argument(
          "--hidden_size",
          type=int,
          default=100,
          help="Dimension of the hidden representation",
      )
      parser.add_argument(
          "--gpu",
          action="store_true",
          help="If set, training is going to use GPU 0",
      )
  
      args = parser.parse_args()
  
      device = core.DeviceOption(
          workspace.GpuDeviceType if args.gpu else caffe2_pb2.CPU, 0
      )
      with core.DeviceScope(device):
          model = CharRNN(args)
          model.CreateModel()
          model.TrainModel()
  
file_path: pytorch/caffe2/python/examples/char_rnn.py
imports: from caffe2.python import core, workspace, model_helper, utils, brew
  from caffe2.python.rnn_cell import LSTM
  from caffe2.proto import caffe2_pb2
  from caffe2.python.optimizer import build_sgd
  import argparse
  import logging
  import numpy
  from datetime import datetime
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types