code: def prepare_config_and_inputs(self, model_class=None):
      config = self.get_config()
  
      input_mask = None
      sequence_labels = None
      token_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.num_labels
          )
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
  
      if (
          model_class is None
          or model_class.__name__ == "PerceiverModel"
      ):
          inputs = floats_tensor(
              [self.batch_size, self.seq_length, config.d_model],
              scale=1.0,
          )
          return (
              config,
              inputs,
              input_mask,
              sequence_labels,
              token_labels,
          )
      elif model_class.__name__ in [
          "PerceiverForMaskedLM",
          "PerceiverForSequenceClassification",
      ]:
          inputs = ids_tensor(
              [self.batch_size, self.seq_length], self.vocab_size
          )
          # input mask is only relevant for text inputs
          if self.use_input_mask:
              input_mask = random_attention_mask(
                  [self.batch_size, self.seq_length]
              )
      elif (
          model_class.__name__
          == "PerceiverForImageClassificationLearned"
      ):
          inputs = floats_tensor(
              [
                  self.batch_size,
                  self.num_channels,
                  self.image_size,
                  self.image_size,
              ]
          )
      elif (
          model_class.__name__
          == "PerceiverForImageClassificationFourier"
      ):
          inputs = floats_tensor(
              [
                  self.batch_size,
                  self.num_channels,
                  self.image_size,
                  self.image_size,
              ]
          )
      elif (
          model_class.__name__
          == "PerceiverForImageClassificationConvProcessing"
      ):
          inputs = floats_tensor(
              [
                  self.batch_size,
                  self.num_channels,
                  self.image_size,
                  self.image_size,
              ]
          )
      elif model_class.__name__ == "PerceiverForOpticalFlow":
          inputs = floats_tensor(
              [
                  self.batch_size,
                  2,
                  27,
                  self.train_size[0],
                  self.train_size[1],
              ]
          )
      elif model_class.__name__ == "PerceiverForMultimodalAutoencoding":
          images = torch.randn(
              (
                  self.batch_size,
                  self.num_frames,
                  self.num_channels,
                  self.image_size,
                  self.image_size,
              ),
              device=torch_device,
          )
          audio = torch.randn(
              (
                  self.batch_size,
                  self.num_frames * self.audio_samples_per_frame,
                  1,
              ),
              device=torch_device,
          )
          inputs = dict(
              image=images,
              audio=audio,
              label=torch.zeros(
                  (self.batch_size, self.num_labels),
                  device=torch_device,
              ),
          )
      else:
          raise ValueError(f"Model class {model_class} not supported")
  
      return config, inputs, input_mask, sequence_labels, token_labels
  
imports: import copy
  import inspect
  import math
  import tempfile
  import unittest
  import warnings
  from typing import Dict, List, Tuple
  import numpy
  from datasets import load_dataset
  from transformers import PerceiverConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the PerceiverModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs(model_class) and returns: config,  inputs,  input_mask,  sequence_labels,  token_labels, config,  inputs,  input_mask,  sequence_labels,  token_labels