code: def test_philox_rand(self, device, dtype):
      sizes = (1000, 1000000)  # offsets of 4 and 8
      repeats = 2  # Checks multiple rand calls results with multiple philox_rand calls
      for size in sizes:
          torch.cuda.manual_seed(123)
          references = []
          results = []
          rng_states = []
          for _ in range(repeats):
              rng_states.append(
                  CUDARngStateHelper.get_torch_state_as_tuple()
              )
              references.append(
                  torch.rand(size, device=device, dtype=dtype)
              )
  
          torch.cuda.manual_seed(123)
          for idx in range(repeats):
              seed, offset = rng_states[idx]
              result, _ = torch.ops.rngprims.philox_rand(
                  (size,),
                  seed=seed,
                  offset=offset,
                  stride=None,
                  device=device,
                  dtype=dtype,
              )
              results.append(result)
  
          for a, b in zip(references, results):
              self.assertEqual(a, b)
  
imports: from functools import partial
  from itertools import product
  import unittest
  import torch
  from torch.testing import make_tensor
  from torch.testing._internal.common_utils import parametrize, run_tests, TestCase, TEST_SCIPY, set_default_dtype
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCUDA, dtypes, OpDTypes
  from torch.testing._internal.common_methods_invocations import op_db
  from torch.testing._internal.common_device_type import ops
  from torch.testing._internal.logging_tensor import LoggingTensor, capture_logs, log_input
  import torch._prims
  from torch._prims_common import CUDARngStateHelper
  from torch._prims.executor import make_traced
  import torch._refs
label_desc: Use image node_id 9 for calling the TestPrims obj's underlying member method code with example usage: obj.test_philox_rand(device, dtype) without return types