code: def test_force_dequant_gelu(self):
      x_int = torch.range(-10000, 10000, 1)
      x_scaling_factor = torch.tensor(0.001)
      x = x_int * x_scaling_factor
  
      gelu_dq = IntGELU(quant_mode=False)
      gelu_fdqs_dict = {
          True: [
              IntGELU(quant_mode=True, force_dequant="nonlinear"),
              IntGELU(quant_mode=True, force_dequant="gelu"),
          ],
          False: [
              IntGELU(quant_mode=True, force_dequant="none"),
              IntGELU(quant_mode=True, force_dequant="softmax"),
              IntGELU(quant_mode=True, force_dequant="layernorm"),
          ],
      }
  
      dq, dq_scaling_factor = gelu_dq(x, x_scaling_factor)
      for label, gelu_fdqs in gelu_fdqs_dict.items():
          for gelu_fdq in gelu_fdqs:
              q, q_scaling_factor = gelu_fdq(x, x_scaling_factor)
              if label:
                  self.assertTrue(torch.allclose(q, dq, atol=1e-4))
              else:
                  self.assertFalse(torch.allclose(q, dq, atol=1e-4))
  
imports: import copy
  import unittest
  from transformers import IBertConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the IBertModelIntegrationTest obj's underlying member method code with example usage: obj.test_force_dequant_gelu() without return types