class_docstr: Constructs a MusicGen processor which wraps an EnCodec feature extractor and a T5 tokenizer into a single processor
  class.
  
  [`MusicgenProcessor`] offers all the functionalities of [`EncodecFeatureExtractor`] and [`TTokenizer`]. See
  [`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information.
  
  Args:
      feature_extractor (`EncodecFeatureExtractor`):
          An instance of [`EncodecFeatureExtractor`]. The feature extractor is a required input.
      tokenizer (`T5Tokenizer`):
          An instance of [`T5Tokenizer`]. The tokenizer is a required input.
docstr: Forwards the `audio` argument to EncodecFeatureExtractor's [`~EncodecFeatureExtractor.__call__`] and the `text`
  argument to [`~T5Tokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
  information.
code: def __call__(self, *args, **kwargs):
      """
      Forwards the `audio` argument to EncodecFeatureExtractor's [`~EncodecFeatureExtractor.__call__`] and the `text`
      argument to [`~T5Tokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
      information.
      """
      # For backward compatibility
      if self._in_target_context_manager:
          return self.current_processor(*args, **kwargs)
  
      audio = kwargs.pop("audio", None)
      sampling_rate = kwargs.pop("sampling_rate", None)
      text = kwargs.pop("text", None)
      if len(args) > 0:
          audio = args[0]
          args = args[1:]
  
      if audio is None and text is None:
          raise ValueError(
              "You need to specify either an `audio` or `text` input to process."
          )
  
      if text is not None:
          inputs = self.tokenizer(text, **kwargs)
  
      if audio is not None:
          audio_inputs = self.feature_extractor(
              audio, *args, sampling_rate=sampling_rate, **kwargs
          )
  
      if audio is None:
          return inputs
  
      elif text is None:
          return audio_inputs
  
      else:
          inputs["input_values"] = audio_inputs["input_values"]
          if "padding_mask" in audio_inputs:
              inputs["padding_mask"] = audio_inputs["padding_mask"]
          return inputs
  
file_path: transformers/src/transformers/models/musicgen/processing_musicgen.py
imports: from typing import List, Optional
  import numpy
  from ...processing_utils import ProcessorMixin
  from ...utils import to_numpy
label_desc: Use image node_id 3 for calling the MusicgenProcessor obj's underlying member method code with example usage: obj.__call__() and returns: self, inputs, audio_inputs, inputs