docstr: A small test to make sure that inference work in half precision without any problem.
docstr_tok: ["A","small","test","to","make","sure","that","inference","work","in","half","precision","without","any","problem","."]
code: def test_inference_fp16(self):
      r"""
      A small test to make sure that inference work in half precision without any problem.
      """
      model = PvtForImageClassification.from_pretrained(
          "Zetatech/pvt-tiny-224", torch_dtype=torch.float16
      )
      model.to(torch_device)
      image_processor = PvtImageProcessor(size=224)
  
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt")
      pixel_values = inputs.pixel_values.to(
          torch_device, dtype=torch.float16
      )
  
      # forward pass to make sure inference works in fp16
      with torch.no_grad():
          _ = model(pixel_values)
  
imports: import unittest
  from transformers import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_accelerate, require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the PvtModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_fp16() without return types