class_docstr: Constructs a MCTCT processor which wraps a MCTCT feature extractor and a MCTCT tokenizer into a single processor.
  
  [`MCTCTProcessor`] offers all the functionalities of [`MCTCTFeatureExtractor`] and [`AutoTokenizer`]. See the
  [`~MCTCTProcessor.__call__`] and [`~MCTCTProcessor.decode`] for more information.
  
  Args:
      feature_extractor (`MCTCTFeatureExtractor`):
          An instance of [`MCTCTFeatureExtractor`]. The feature extractor is a required input.
      tokenizer (`AutoTokenizer`):
          An instance of [`AutoTokenizer`]. The tokenizer is a required input.
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docstr: When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
  [`~MCTCTFeatureExtractor.pad`] and returns its output. If used in the context
  [`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
  [`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information.
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code: def pad(self, *args, **kwargs):
      """
      When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
      [`~MCTCTFeatureExtractor.pad`] and returns its output. If used in the context
      [`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
      [`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information.
      """
      # For backward compatibility
      if self._in_target_context_manager:
          return self.current_processor.pad(*args, **kwargs)
  
      input_features = kwargs.pop("input_features", None)
      labels = kwargs.pop("labels", None)
      if len(args) > 0:
          input_features = args[0]
          args = args[1:]
  
      if input_features is not None:
          input_features = self.feature_extractor.pad(
              input_features, *args, **kwargs
          )
      if labels is not None:
          labels = self.tokenizer.pad(labels, **kwargs)
  
      if labels is None:
          return input_features
      elif input_features is None:
          return labels
      else:
          input_features["labels"] = labels["input_ids"]
          return input_features
  
file_path: transformers/src/transformers/models/deprecated/mctct/processing_mctct.py
imports: import warnings
  from contextlib import contextmanager
  from ....processing_utils import ProcessorMixin
label_desc: Use image node_id 4 for calling the MCTCTProcessor obj's underlying member method code with example usage: obj.pad() and returns: self, input_features, labels, input_features