class_docstr: Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.
  
  [`ClapProcessor`] offers all the functionalities of [`ClapFeatureExtractor`] and [`RobertaTokenizerFast`]. See the
  [`~ClapProcessor.__call__`] and [`~ClapProcessor.decode`] for more information.
  
  Args:
      feature_extractor ([`ClapFeatureExtractor`]):
          The audio processor is a required input.
      tokenizer ([`RobertaTokenizerFast`]):
          The tokenizer is a required input.
code: def model_input_names(self):
      tokenizer_input_names = self.tokenizer.model_input_names
      feature_extractor_input_names = (
          self.feature_extractor.model_input_names
      )
      return list(
          dict.fromkeys(
              tokenizer_input_names + feature_extractor_input_names
          )
      )
  
file_path: transformers/src/transformers/models/clap/processing_clap.py
imports: from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
label_desc: Use image node_id 5 for calling the ClapProcessor obj's underlying member method code with example usage: obj.model_input_names() and returns: list