class_docstr: Provide an opt-producer and CLI arguement parser.
  
  More options can be added specific by paassing this object and calling
  ''add_arg()'' or add_argument'' on it.
  
  :param add_blink_args:
      (default True) initializes the default arguments for BLINK package.
  :param add_model_args:
      (default False) initializes the default arguments for loading models,
      including initializing arguments from the model.
class_docstr_tok: ["Provide","an","opt-producer","and","CLI","arguement","parser",".","More","options","can","be","added","specific","by","paassing","this","object","and","calling","''","add_arg","(",")","''","or","add_argument","''","on","it",".",":","param","add_blink_args",":","(","default","True",")","initializes","the","default","arguments","for","BLINK","package",".",":","param","add_model_args",":","(","default","False",")","initializes","the","default","arguments","for","loading","models",",","including","initializing","arguments","from","the","model","."]
docstr: Add model training args.
docstr_tok: ["Add","model","training","args","."]
code: def add_training_args(self, args=None):
      """
      Add model training args.
      """
      parser = self.add_argument_group("Model Training Arguments")
      parser.add_argument(
          "--evaluate",
          action="store_true",
          help="Whether to run evaluation.",
      )
      parser.add_argument(
          "--output_eval_file",
          default=None,
          type=str,
          help="The txt file where the the evaluation results will be written.",
      )
      parser.add_argument(
          "--train_batch_size",
          default=8,
          type=int,
          help="Total batch size for training.",
      )
      parser.add_argument("--max_grad_norm", default=1.0, type=float)
      parser.add_argument(
          "--learning_rate",
          default=3e-5,
          type=float,
          help="The initial learning rate for Adam.",
      )
      parser.add_argument(
          "--num_train_epochs",
          default=1,
          type=int,
          help="Number of training epochs.",
      )
      parser.add_argument(
          "--print_interval",
          type=int,
          default=10,
          help="Interval of loss printing",
      )
      parser.add_argument(
          "--eval_interval",
          type=int,
          default=100,
          help="Interval for evaluation during training",
      )
      parser.add_argument(
          "--save_interval",
          type=int,
          default=1,
          help="Interval for model saving",
      )
      parser.add_argument(
          "--warmup_proportion",
          default=0.1,
          type=float,
          help="Proportion of training to perform linear learning rate warmup for. "
          "E.g., 0.1 = 10% of training.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumualte before performing a backward/update pass.",
      )
      parser.add_argument(
          "--type_optimization",
          type=str,
          default="all_encoder_layers",
          help="Which type of layers to optimize in BERT",
      )
      parser.add_argument(
          "--shuffle",
          type=bool,
          default=False,
          help="Whether to shuffle train data",
      )
  
file_path: BLINK/blink/common/params.py
imports: import argparse
  import importlib
  import os
  import sys
  import datetime
label_desc: Use image node_id 4 for calling the BlinkParser obj's underlying member method code with example usage: obj.add_training_args(args) without return types