docstr: Verify that we can populate and load models from the cache.
docstr_tok: ["Verify","that","we","can","populate","and","load","models","from","the","cache","."]
code: def test_cache_load_model(self, device, dtype, dynamic):
      """
      Verify that we can populate and load models from the cache.
      """
      if device == "cuda" and not HAS_CUDA:
          raise unittest.SkipTest("requires CUDA")
  
      def fn(mod, x):
          mod.zero_grad()
          mod(x).sum().backward()
          return [p.grad for p in mod.parameters()]
  
      compiled_fn = torch.compile(fn, dynamic=dynamic)
  
      mod = MyModelConv2d().to(device=device, dtype=dtype)
      inp = torch.randn(2, 3, 16, 16, device=device, dtype=dtype)
  
      # The first call should see all cache misses.
      counters.clear()
      grads1 = compiled_fn(mod, inp)
      self.assertGreater(counters["inductor"]["fxgraph_cache_miss"], 0)
      self.assertEqual(counters["inductor"]["fxgraph_cache_hit"], 0)
  
      # The second should see all hits. (First reset so in-memory guards
      # don't prevent compilation).
      counters.clear()
      torch._dynamo.reset()
      grads2 = compiled_fn(mod, inp)
      self.assertEqual(counters["inductor"]["fxgraph_cache_miss"], 0)
      self.assertGreater(counters["inductor"]["fxgraph_cache_hit"], 0)
  
      # And the results should be the same.
      self.assertEqual(grads1, grads2)
  
imports: import functools
  import pickle
  import tempfile
  import unittest
  from unittest.mock import patch
  import torch
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch._inductor import config
  from torch._inductor.codecache import AsyncCompile, FxGraphCachePickler, FxGraphHashDetails, TensorMetadata, TensorMetadataAndValues
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_device_type import largeTensorTest
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize
  from torch.testing._internal.inductor_utils import HAS_CUDA
  from torch.utils._triton import has_triton
label_desc: Use image node_id 5 for calling the TestFxGraphCache obj's underlying member method code with example usage: obj.test_cache_load_model(device, dtype, dynamic) and returns: unknown