class_docstr: Handles arguments for zero-shot for text classification by turning each possible label into an NLI
  premise/hypothesis pair.
class_docstr_tok: ["Handles","arguments","for","zero-shot","for","text","classification","by","turning","each","possible","label","into","an","NLI","premise\/hypothesis","pair","."]
code: def __call__(self, sequences, labels, hypothesis_template):
      if len(labels) == 0 or len(sequences) == 0:
          raise ValueError(
              "You must include at least one label and at least one sequence."
          )
      if hypothesis_template.format(labels[0]) == hypothesis_template:
          raise ValueError(
              (
                  'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
                  "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
              ).format(hypothesis_template)
          )
  
      if isinstance(sequences, str):
          sequences = [sequences]
  
      sequence_pairs = []
      for sequence in sequences:
          sequence_pairs.extend(
              [
                  [sequence, hypothesis_template.format(label)]
                  for label in labels
              ]
          )
  
      return sequence_pairs, sequences
  
file_path: transformers/src/transformers/pipelines/zero_shot_classification.py
imports: import inspect
  from typing import List, Union
  import numpy
  from ..tokenization_utils import TruncationStrategy
  from ..utils import add_end_docstrings, logging
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
label_desc: Use image node_id 2 for calling the ZeroShotClassificationArgumentHandler obj's underlying member method code with example usage: obj.__call__(sequences, labels, hypothesis_template) and returns: sequence_pairs,  sequences