code: def convert_dataset_for_tensorflow(
      dataset,
      batch_size,
      dataset_mode="variable_batch",
      shuffle=True,
      drop_remainder=True,
  ):
      """Converts a Hugging Face dataset to a Tensorflow Dataset. The dataset_mode controls whether we pad all batches
      to the maximum sequence length, or whether we only pad to the maximum length within that batch. The former
      is most useful when training on TPU, as a new graph compilation is required for each sequence length.
      """
      try:
          from datasets import Dataset, load_dataset
      except ImportError:
          raise ImportError(
              "The datasets package is required for fine-tuning QA models: pip install datasets"
          )
  
      def densify_ragged_batch(features, label=None):
          features = {
              feature: ragged_tensor.to_tensor(
                  shape=batch_shape[feature]
              )
              if feature in tensor_keys
              else ragged_tensor
              for feature, ragged_tensor in features.items()
          }
          if label is None:
              return features
          else:
              return features, label
  
      tensor_keys = ["attention_mask", "input_ids"]
      label_keys = ["start_positions", "end_positions"]
      if dataset_mode == "variable_batch":
          batch_shape = {key: None for key in tensor_keys}
          data = {
              key: tf.ragged.constant(dataset[key])
              for key in tensor_keys
          }
      elif dataset_mode == "constant_batch":
          data = {
              key: tf.ragged.constant(dataset[key])
              for key in tensor_keys
          }
          batch_shape = {
              key: tf.concat(
                  ([batch_size], ragged_tensor.bounding_shape()[1:]),
                  axis=0,
              )
              for key, ragged_tensor in data.items()
          }
      else:
          raise ValueError("Unknown dataset mode!")
  
      if all([key in dataset.features for key in label_keys]):
          for key in label_keys:
              data[key] = tf.convert_to_tensor(dataset[key])
          dummy_labels = tf.zeros_like(dataset[key])
          tf_dataset = tf.data.Dataset.from_tensor_slices(
              (data, dummy_labels)
          )
      else:
          tf_dataset = tf.data.Dataset.from_tensor_slices(data)
      if shuffle:
          tf_dataset = tf_dataset.shuffle(buffer_size=len(dataset))
      tf_dataset = tf_dataset.batch(
          batch_size=batch_size, drop_remainder=drop_remainder
      ).map(densify_ragged_batch)
      return tf_dataset
  
file_path: ktrain/ktrain/text/qa/qa_finetuner.py
imports: import warnings
  import pandas
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: convert_dataset_for_tensorflow(dataset, batch_size, dataset_mode, shuffle, drop_remainder) and returns: tf_dataset, features, features,  label