code: def prepare_inputs_dict(
      config,
      input_ids=None,
      input_values=None,
      decoder_input_ids=None,
      attention_mask=None,
      decoder_attention_mask=None,
      head_mask=None,
      decoder_head_mask=None,
      cross_attn_head_mask=None,
  ):
      if input_ids is not None:
          encoder_dict = {"input_ids": input_ids}
      else:
          encoder_dict = {"input_values": input_values}
  
      decoder_dict = (
          {"decoder_input_ids": decoder_input_ids}
          if decoder_input_ids is not None
          else {}
      )
  
      return {**encoder_dict, **decoder_dict}
  
imports: import copy
  import inspect
  import os
  import tempfile
  import unittest
  from typing import Dict, List, Tuple
  import numpy
  from datasets import Audio, load_dataset
  from transformers import AutoProcessor, EncodecConfig
  from transformers.testing_utils import is_torch_available, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_inputs_dict(config, input_ids, input_values, decoder_input_ids, attention_mask, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask) and returns: dict