code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = super()._prepare_for_class(
          inputs_dict, model_class, return_labels=return_labels
      )
  
      #  if PatchTSTForPretraining
      if model_class == PatchTSTForPretraining:
          inputs_dict.pop("future_values")
      # else if classification model:
      elif model_class in get_values(
          MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING
      ):
          rng = random.Random(self.model_tester.seed)
          labels = ids_tensor(
              [self.model_tester.batch_size],
              self.model_tester.num_targets,
              rng=rng,
          )
          inputs_dict["target_values"] = labels
          inputs_dict.pop("future_values")
      elif model_class in get_values(
          MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING
      ):
          rng = random.Random(self.model_tester.seed)
          target_values = floats_tensor(
              [
                  self.model_tester.batch_size,
                  self.model_tester.num_targets,
              ],
              rng=rng,
          )
          inputs_dict["target_values"] = target_values
          inputs_dict.pop("future_values")
      return inputs_dict
  
imports: import inspect
  import random
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the PatchTSTModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict