class_docstr: `BetterTransformersTestMixin` to wrap necessary functions for testing `BetterTransformer`
  integration. This includes the following tests:
      - `test_logits`: This tests if the converted model produces the same logits
      than the original model.
      - `test_raise_on_save`: Test if the converion properly raises an error if someone tries to save the model using `save_pretrained`.
class_docstr_tok: ["`","BetterTransformersTestMixin","`","to","wrap","necessary","functions","for","testing","`","BetterTransformer","`","integration",".","This","includes","the","following","tests",":","-","`","test_logits","`",":","This","tests","if","the","converted","model","produces","the","same","logits","than","the","original","model",".","-","`","test_raise_on_save","`",":","Test","if","the","converion","properly","raises","an","error","if","someone","tries","to","save","the","model","using","`","save_pretrained","`","."]
docstr: This tests if the converted model runs fine under fp16.
docstr_tok: ["This","tests","if","the","converted","model","runs","fine","under","fp16","."]
code: def _test_fp16_inference(
      self,
      model_id: str,
      model_type: str,
      automodel_class,
      use_to_operator=False,
      **preprocessor_kwargs,
  ):
      r"""
      This tests if the converted model runs fine under fp16.
      """
      # The first row of the attention mask needs to be all ones -> check: https://github.com/pytorch/pytorch/blob/19171a21ee8a9cc1a811ac46d3abd975f0b6fc3b/test/test_nn.py#L5283
      inputs = self.prepare_inputs_for_class(
          model_id=model_id,
          model_type=model_type,
          **preprocessor_kwargs,
      ).to(0)
  
      torch.manual_seed(0)
      if not use_to_operator:
          hf_random_model = automodel_class.from_pretrained(
              model_id, torch_dtype=torch.float16
          ).to(0)
          converted_model = BetterTransformer.transform(
              hf_random_model, keep_original_model=True
          )
      else:
          hf_random_model = automodel_class.from_pretrained(
              model_id
          ).to(0)
          converted_model = BetterTransformer.transform(
              hf_random_model, keep_original_model=True
          )
          hf_random_model = hf_random_model.to(torch.float16)
          converted_model = converted_model.to(torch.float16)
  
      self.assertFalse(
          hasattr(hf_random_model, "use_bettertransformer"),
          f"The model {hf_random_model.__class__.__name__} has been converted to a `fast` model by mistake.",
      )
  
      length = 50
      with torch.no_grad():
          r"""
          Make sure the models are in eval mode! Make also sure that the original model
          has not been converted to a fast model. The check is done above.
          """
          torch.manual_seed(0)
          output_hf = hf_random_model.generate(
              **inputs, min_length=length, max_length=length
          )
  
          torch.manual_seed(0)
          output_bt = converted_model.generate(
              **inputs, min_length=length, max_length=length
          )
  
          self.assertTrue(
              torch.allclose(output_hf, output_bt),
              f"Maxdiff: {(output_hf - output_bt).abs().max()}",
          )
  
imports: import copy
  import inspect
  import tempfile
  import unittest
  import torch
  from transformers import AutoModel
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils.testing_utils import flatten_dict, require_torch_gpu
label_desc: Use image node_id 2 for calling the BetterTransformersTestMixin obj's underlying member method code with example usage: obj._test_fp16_inference(model_id, model_type, automodel_class, use_to_operator) without return types