code: def transform(
      self,
      samples: TextSamplesVar,
      *,
      seq_length: int = None,
      max_position: int = None,
      segment: bool = False,
  ) -> np.ndarray:
      if self.multi_label:
          sample_tensor = self.multi_label_binarizer.transform(samples)
          return sample_tensor
  
      sample_tensor = [self.vocab2idx[i] for i in samples]
      return np.array(sample_tensor)
  
file_path: Kashgari/kashgari/processors/class_processor.py
imports: import collections
  import operator
  from typing import List, Union, Dict, Optional, Any, Tuple
  import numpy
  import tqdm
  from kashgari.generators import CorpusGenerator
  from kashgari.processors.abc_processor import ABCProcessor
  from kashgari.types import TextSamplesVar
label_desc: Use image node_id 5 for calling the ClassificationProcessor obj's underlying member method code with example usage: obj.transform(samples) and returns: np, sample_tensor