class_docstr: Here we also overwrite some of the tests of test_modeling_common.py, as TVP does not use, inputs_embeds.
  The seq_length in TVP contain textual and visual inputs, and prompt.
class_docstr_tok: ["Here","we","also","overwrite","some","of","the","tests","of","test_modeling_common.py",",","as","TVP","does","not","use",",","inputs_embeds",".","The","seq_length","in","TVP","contain","textual","and","visual","inputs",",","and","prompt","."]
code: def test_initialization(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      configs_no_init = _config_zero_init(config)
      for model_class in self.all_model_classes:
          model = model_class(config=configs_no_init)
          for name, param in model.named_parameters():
              if param.requires_grad:
                  # params are randomly initialized.
                  self.assertAlmostEqual(
                      param.data.mean().item(),
                      0.0,
                      delta=1.0,
                      msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                  )
  
imports: import unittest
  from transformers import ResNetConfig, TvpConfig
  from transformers.testing_utils import require_torch, require_vision, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the TVPModelTest obj's underlying member method code with example usage: obj.test_initialization() without return types