code: def test_equivalence_pt_to_flax(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__):
              # prepare inputs
              prepared_inputs_dict = self._prepare_for_class(
                  inputs_dict, model_class
              )
              pt_inputs = {
                  k: torch.tensor(v.tolist())
                  for k, v in prepared_inputs_dict.items()
              }
  
              # load corresponding PyTorch class
              pt_model_class_name = model_class.__name__[
                  4:
              ]  # Skip the "Flax" at the beginning
              pt_model_class = getattr(
                  transformers, pt_model_class_name
              )
  
              batch_size, seq_length = pt_inputs["input_ids"].shape
              rnd_start_indices = np.random.randint(
                  0, seq_length - 1, size=(batch_size,)
              )
              for batch_idx, start_index in enumerate(
                  rnd_start_indices
              ):
                  pt_inputs["attention_mask"][
                      batch_idx, :start_index
                  ] = 0
                  pt_inputs["attention_mask"][
                      batch_idx, start_index:
                  ] = 1
                  prepared_inputs_dict["attention_mask"][
                      batch_idx, :start_index
                  ] = 0
                  prepared_inputs_dict["attention_mask"][
                      batch_idx, start_index:
                  ] = 1
              pt_model = pt_model_class(config).eval()
              fx_model = model_class(config, dtype=jnp.float32)
  
              fx_state = convert_pytorch_state_dict_to_flax(
                  pt_model.state_dict(), fx_model
              )
              fx_model.params = fx_state
  
              with torch.no_grad():
                  pt_outputs = pt_model(**pt_inputs).to_tuple()
  
              fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
              self.assertEqual(
                  len(fx_outputs),
                  len(pt_outputs),
                  "Output lengths differ between Flax and PyTorch",
              )
              for fx_output, pt_output in zip(fx_outputs, pt_outputs):
                  self.assert_almost_equals(
                      fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2
                  )
  
              with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_model.save_pretrained(tmpdirname)
                  fx_model_loaded = model_class.from_pretrained(
                      tmpdirname, from_pt=True
                  )
  
              fx_outputs_loaded = fx_model_loaded(
                  **prepared_inputs_dict
              ).to_tuple()
              self.assertEqual(
                  len(fx_outputs_loaded),
                  len(pt_outputs),
                  "Output lengths differ between Flax and PyTorch",
              )
              for fx_output_loaded, pt_output in zip(
                  fx_outputs_loaded, pt_outputs
              ):
                  self.assert_almost_equals(
                      fx_output_loaded[:, -1],
                      pt_output[:, -1].numpy(),
                      4e-2,
                  )
  
imports: import tempfile
  import unittest
  import numpy
  import transformers
  from transformers import GPT2Tokenizer, GPTJConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the FlaxGPTJModelTest obj's underlying member method code with example usage: obj.test_equivalence_pt_to_flax() without return types