code: def test_problem_types(self):
      problem_types = [
          {
              "title": "multi_label_classification",
              "num_labels": 2,
              "dtype": torch.float,
          },
          {
              "title": "single_label_classification",
              "num_labels": 1,
              "dtype": torch.long,
          },
          {
              "title": "regression",
              "num_labels": 1,
              "dtype": torch.float,
          },
      ]
  
      for model_class in self.all_model_classes:
          if model_class not in get_values(
              MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
          ):
              continue
  
          (
              config,
              inputs,
              input_mask,
              _,
              _,
          ) = self.model_tester.prepare_config_and_inputs(
              model_class=model_class
          )
          inputs_dict = dict(inputs=inputs, attention_mask=input_mask)
  
          for problem_type in problem_types:
              with self.subTest(
                  msg=f"Testing {model_class} with {problem_type['title']}"
              ):
                  config.problem_type = problem_type["title"]
                  config.num_labels = problem_type["num_labels"]
  
                  model = model_class(config)
                  model.to(torch_device)
                  model.train()
  
                  inputs = self._prepare_for_class(
                      inputs_dict, model_class, return_labels=True
                  )
  
                  if problem_type["num_labels"] > 1:
                      inputs["labels"] = (
                          inputs["labels"]
                          .unsqueeze(1)
                          .repeat(1, problem_type["num_labels"])
                      )
  
                  inputs["labels"] = inputs["labels"].to(
                      problem_type["dtype"]
                  )
  
                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                  # they have the same size." which is a symptom something in wrong for the regression problem.
                  # See https://github.com/huggingface/transformers/issues/11780
                  with warnings.catch_warnings(
                      record=True
                  ) as warning_list:
                      loss = model(**inputs).loss
                  for w in warning_list:
                      if (
                          "Using a target size that is different to the input size"
                          in str(w.message)
                      ):
                          raise ValueError(
                              f"Something is going wrong in the regression problem: intercepted {w.message}"
                          )
  
                  loss.backward()
  
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 20 for calling the PerceiverModelTest obj's underlying member method code with example usage: obj.test_problem_types() without return types