code: def create_generic_data_row_from_tokens_and_segments(
      guid: str,
      unpadded_tokens: List[str],
      unpadded_segment_ids: List[int],
      label_id: int,
      tokenizer,
      feat_spec: FeaturizationSpec,
      data_row_class,
  ):
      """Creates an InputSet and wraps the InputSet into a DataRow class.
  
      Args:
          guid (str): human-readable identifier (for interpretability and debugging).
          unpadded_tokens (List[str]): sequence of unpadded token strings.
          unpadded_segment_ids (List[int]): sequence of unpadded segment ids.
          label_id (int): int representing the label for the task.
          tokenizer:
          feat_spec (FeaturizationSpec): Tokenization-related metadata.
          data_row_class (DataRow): data row class to wrap and return the inputs.
  
      Returns:
          DataRow: data row class containing model inputs.
  
      """
      input_set = create_input_set_from_tokens_and_segments(
          unpadded_tokens=unpadded_tokens,
          unpadded_segment_ids=unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
      return data_row_class(
          guid=guid,
          input_ids=np.array(input_set.input_ids),
          input_mask=np.array(input_set.input_mask),
          segment_ids=np.array(input_set.segment_ids),
          label_id=label_id,
          tokens=unpadded_tokens,
      )
  
imports: import numpy
  from dataclasses import dataclass
  from typing import List, NamedTuple
  from jiant.tasks.core import FeaturizationSpec
  from jiant.tasks.utils import truncate_sequences, pad_to_max_seq_length
  from jiant.utils.python.datastructures import BiMap
label_desc: Use image node_id 6 for calling a global function with example usage: create_generic_data_row_from_tokens_and_segments(guid, unpadded_tokens, unpadded_segment_ids, label_id, tokenizer, feat_spec, data_row_class) and returns: data_row_class