class_docstr: Wrapper class for Hugging Face Transformers PyTorch T5 model.
class_docstr_tok: ["Wrapper","class","for","Hugging","Face","Transformers","PyTorch","T5","model","."]
docstr: Retrieve the step corresponding to the most recent checkpoint.
  
  Args:
    model_dir: str, the directory of the checkpoints or None to use this
      model's directory.
  
  Returns:
    An integer corresponding to the most recent step, or None if there are no
    checkpoints in the model directory.
docstr_tok: ["Retrieve","the","step","corresponding","to","the","most","recent","checkpoint",".","Args",":","model_dir",":","str",",","the","directory","of","the","checkpoints","or","None","to","use","this","model","'s","directory",".","Returns",":","An","integer","corresponding","to","the","most","recent","step",",","or","None","if","there","are","no","checkpoints","in","the","model","directory","."]
code: def get_latest_checkpoint_step(self, model_dir=None):
      """Retrieve the step corresponding to the most recent checkpoint.
  
      Args:
        model_dir: str, the directory of the checkpoints or None to use this
          model's directory.
  
      Returns:
        An integer corresponding to the most recent step, or None if there are no
        checkpoints in the model directory.
      """
      steps = self.get_all_checkpoint_steps(model_dir)
      if steps is not None:
          return max(steps)
  
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 7 for calling the HfPyTorchModel obj's underlying member method code with example usage: obj.get_latest_checkpoint_step(model_dir) and returns: max