code: def test_model_for_input_embeds(self):
      batch_size = 2
      seq_length = 10
      inputs_embeds = torch.rand(
          [batch_size, seq_length, 768], device=torch_device
      )
      config = self.model_tester.get_config()
      model = ConvBertModel(config=config)
      model.to(torch_device)
      model.eval()
      result = model(inputs_embeds=inputs_embeds)
      self.assertEqual(
          result.last_hidden_state.shape,
          (batch_size, seq_length, config.hidden_size),
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import ConvBertConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 12 for calling the ConvBertModelTest obj's underlying member method code with example usage: obj.test_model_for_input_embeds() without return types