class_docstr: Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
  
  [`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
  the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
  
  Args:
      image_processor (`IdeficsImageProcessor`):
          An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
      tokenizer (`LlamaTokenizerFast`):
          An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
      image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
code: def model_input_names(self):
      tokenizer_input_names = self.tokenizer.model_input_names
      image_processor_input_names = (
          self.image_processor.model_input_names
      )
      return list(
          dict.fromkeys(
              tokenizer_input_names + image_processor_input_names
          )
      )
  
file_path: transformers/src/transformers/models/idefics/processing_idefics.py
imports: from typing import Callable, List, Optional, Union
  from urllib.parse import urlparse
  from ...feature_extraction_utils import BatchFeature
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
  from ...utils import TensorType, is_torch_available
label_desc: Use image node_id 5 for calling the IdeficsProcessor obj's underlying member method code with example usage: obj.model_input_names() and returns: list