code: def create_and_check_fourier_transform(self, config):
      hidden_states = floats_tensor(
          [self.batch_size, self.seq_length, config.hidden_size]
      )
      transform = FNetBasicFourierTransform(config)
      fftn_output = transform(hidden_states)
  
      config.use_tpu_fourier_optimizations = True
      if is_scipy_available():
          transform = FNetBasicFourierTransform(config)
          dft_output = transform(hidden_states)
  
      config.max_position_embeddings = 4097
      transform = FNetBasicFourierTransform(config)
      fft_output = transform(hidden_states)
  
      if is_scipy_available():
          self.parent.assertTrue(
              torch.allclose(
                  fftn_output[0][0], dft_output[0][0], atol=1e-4
              )
          )
          self.parent.assertTrue(
              torch.allclose(
                  fft_output[0][0], dft_output[0][0], atol=1e-4
              )
          )
      self.parent.assertTrue(
          torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4)
      )
  
imports: import unittest
  from typing import Dict, List, Tuple
  from transformers import FNetConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 4 for calling the FNetModelTester obj's underlying member method code with example usage: obj.create_and_check_fourier_transform(config) without return types