code: def parse_args():
      import argparse
  
      parser = argparse.ArgumentParser(
          description="DocTR training script for character classification (TensorFlow)",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
  
      parser.add_argument(
          "arch", type=str, help="text-recognition model to train"
      )
      parser.add_argument(
          "--name",
          type=str,
          default=None,
          help="Name of your training experiment",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=10,
          help="number of epochs to train the model on",
      )
      parser.add_argument(
          "-b",
          "--batch_size",
          type=int,
          default=64,
          help="batch size for training",
      )
      parser.add_argument(
          "--input_size",
          type=int,
          default=32,
          help="input size H for the model, W = 4*H",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          help="learning rate for the optimizer (Adam)",
      )
      parser.add_argument(
          "-j",
          "--workers",
          type=int,
          default=None,
          help="number of workers used for dataloading",
      )
      parser.add_argument(
          "--resume",
          type=str,
          default=None,
          help="Path to your checkpoint",
      )
      parser.add_argument(
          "--font",
          type=str,
          default="FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
          help="Font family to be used",
      )
      parser.add_argument(
          "--vocab",
          type=str,
          default="french",
          help="Vocab to be used for training",
      )
      parser.add_argument(
          "--train-samples",
          dest="train_samples",
          type=int,
          default=1000,
          help="Multiplied by the vocab length gets you the number of training samples that will be used.",
      )
      parser.add_argument(
          "--val-samples",
          dest="val_samples",
          type=int,
          default=20,
          help="Multiplied by the vocab length gets you the number of validation samples that will be used.",
      )
      parser.add_argument(
          "--test-only",
          dest="test_only",
          action="store_true",
          help="Run the validation loop",
      )
      parser.add_argument(
          "--show-samples",
          dest="show_samples",
          action="store_true",
          help="Display unormalized training samples",
      )
      parser.add_argument(
          "--wb",
          dest="wb",
          action="store_true",
          help="Log to Weights & Biases",
      )
      parser.add_argument(
          "--clearml",
          dest="clearml",
          action="store_true",
          help="Log to ClearML",
      )
      parser.add_argument(
          "--push-to-hub",
          dest="push_to_hub",
          action="store_true",
          help="Push to Huggingface Hub",
      )
      parser.add_argument(
          "--pretrained",
          dest="pretrained",
          action="store_true",
          help="Load pretrained parameters before starting the training",
      )
      parser.add_argument(
          "--export-onnx",
          dest="export_onnx",
          action="store_true",
          help="Export the model to ONNX",
      )
      parser.add_argument(
          "--amp",
          dest="amp",
          help="Use Automatic Mixed Precision",
          action="store_true",
      )
      parser.add_argument(
          "--find-lr",
          action="store_true",
          help="Gridsearch the optimal LR",
      )
      parser.add_argument(
          "--early-stop",
          action="store_true",
          help="Enable early stopping",
      )
      parser.add_argument(
          "--early-stop-epochs",
          type=int,
          default=5,
          help="Patience for early stopping",
      )
      parser.add_argument(
          "--early-stop-delta",
          type=float,
          default=0.01,
          help="Minimum Delta for early stopping",
      )
      args = parser.parse_args()
  
      return args
  
file_path: doctr/references/classification/train_tensorflow.py
imports: import os
  import datetime
  import multiprocessing
  import time
  import numpy
  import tensorflow
  from tensorflow.keras import mixed_precision
  from tqdm.auto import tqdm
  from doctr.models import login_to_hub, push_to_hf_hub
  from doctr import transforms
  from doctr.datasets import VOCABS, CharacterGenerator, DataLoader
  from doctr.models import classification
  from doctr.models.utils import export_model_to_onnx
  from utils import EarlyStopper, plot_recorder, plot_samples
label_desc: Use image node_id 6 for calling a global function with example usage: parse_args() and returns: args