code: def run_tf(self):
      self.pipeline.fit(
          self.train_dataset,
          validation_data=self.valid_dataset,
          validation_split=self.validation_split,
          learning_rate=self.learning_rate,
          adam_epsilon=self.adam_epsilon,
          train_batch_size=self.train_batch_size,
          valid_batch_size=self.valid_batch_size,
      )
  
      # Save trained pipeline
      self.pipeline.save_pretrained(self.output)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/commands/train.py
imports: import os
  from argparse import ArgumentParser, Namespace
  from ..data import SingleSentenceClassificationProcessor
  from ..pipelines import TextClassificationPipeline
  from ..utils import is_tf_available, is_torch_available, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 5 for calling the TrainCommand obj's underlying member method code with example usage: obj.run_tf() without return types