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 _parse_labels(self, labels):
      if isinstance(labels, str):
          labels = [
              label.strip()
              for label in labels.split(",")
              if label.strip()
          ]
      return labels
  
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 1 for calling the ZeroShotClassificationArgumentHandler obj's underlying member method code with example usage: obj._parse_labels(labels) and returns: labels