class_docstr: Generic processors for the sequence samples.
class_docstr_tok: ["Generic","processors","for","the","sequence","samples","."]
code: def inverse_transform(
      self,  # type: ignore[override]
      labels: Union[List[List[int]], np.ndarray],
      *,
      lengths: List[int] = None,
      threshold: float = 0.5,
      **kwargs: Any,
  ) -> List[List[str]]:
      result = []
      for index, seq in enumerate(labels):
          labels_ = []
          for idx in seq:
              labels_.append(self.idx2vocab[idx])
          if lengths is not None:
              labels_ = labels_[1 : lengths[index] + 1]
          else:
              labels_ = labels_[1:-1]
          result.append(labels_)
      return result
  
file_path: Kashgari/kashgari/processors/sequence_processor.py
imports: import collections
  import operator
  from typing import Dict, List, Any, Optional, Union
  import numpy
  import tqdm
  from tensorflow.keras.preprocessing.sequence import pad_sequences
  from kashgari.generators import CorpusGenerator
  from kashgari.logger import logger
  from kashgari.processors.abc_processor import ABCProcessor
  from kashgari.types import TextSamplesVar
label_desc: Use image node_id 5 for calling the SequenceProcessor obj's underlying member method code with example usage: obj.inverse_transform(labels) and returns: result