code: def _get_dataset_features(dataset, tokenizer, columns=[]):
      if transformers.__version__ > "4.12.0":
          features = {x: dataset[x] for x in columns}
      else:
          features = {
              x: dataset[x].to_tensor(
                  default_value=0,
                  shape=[None, tokenizer.model_max_length],
              )
              for x in columns
          }
  
      return features
  
file_path: sagemaker-python-sdk/tests/data/huggingface/run_tf.py
imports: import argparse
  import logging
  import sys
  import time
  import tensorflow
  import transformers
  from datasets import load_dataset
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
label_desc: Use image node_id 1 for calling a global function with example usage: _get_dataset_features(dataset, tokenizer, columns) and returns: features