class_docstr: Wrapper class for Hugging Face Transformers PyTorch T5 model.
class_docstr_tok: ["Wrapper","class","for","Hugging","Face","Transformers","PyTorch","T5","model","."]
docstr: Save the current model parameters to the `model_dir`.
  
  Args:
    step: int, the current training step.
docstr_tok: ["Save","the","current","model","parameters","to","the","`","model_dir","`",".","Args",":","step",":","int",",","the","current","training","step","."]
code: def save_checkpoint(self, step):
      """Save the current model parameters to the `model_dir`.
  
      Args:
        step: int, the current training step.
      """
      path = os.path.join(
          self._model_dir, CHECKPOINT_FILE_FORMAT.format(step)
      )
      torch.save(self._model.state_dict(), path)
  
imports: import functools
  import itertools
  import os
  import re
  import time
  from absl import logging
  import mesh_tensorflow.transformer.dataset
  import seqio
  import t5.data
  from t5.models import utils
  from t5.models.t5_model import T5Model
  import tensorflow.compat.v1
  import tensorflow_datasets
  import torch
  import torch.utils.tensorboard
label_desc: Use image node_id 4 for calling the HfPyTorchModel obj's underlying member method code with example usage: obj.save_checkpoint(step) without return types