class_docstr: Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
  
  [`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
  [`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
  
  Args:
      image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input.
      tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input.
class_docstr_tok: ["Constructs","a","FLAVA","processor","which","wraps","a","FLAVA","image","processor","and","a","FLAVA","tokenizer","into","a","single","processor",".","[","`","FlavaProcessor","`","]","offers","all","the","functionalities","of","[","`","FlavaImageProcessor","`","]","and","[","`","BertTokenizerFast","`","]",".","See","the","[","`","~FlavaProcessor.__call__","`","]","and","[","`","~FlavaProcessor.decode","`","]","for","more","information",".","Args",":","image_processor","(","[","`","FlavaImageProcessor","`","]",",","*","optional","*",")",":","The","image","processor","is","a","required","input",".","tokenizer","(","[","`","BertTokenizerFast","`","]",",","*","optional","*",")",":","The","tokenizer","is","a","required","input","."]
code: def feature_extractor_class(self):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
          FutureWarning,
      )
      return self.image_processor_class
  
file_path: transformers/src/transformers/models/flava/processing_flava.py
imports: import warnings
  from typing import List, Optional, Union
  from ...image_utils import ImageInput
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
label_desc: Use image node_id 6 for calling the FlavaProcessor obj's underlying member method code with example usage: obj.feature_extractor_class() and returns: self