code: def test_lazy_init_with_view(self):
      def f(device, reset_storage=False):
          torch.manual_seed(2023)
  
          if device == "lazy":
              metrics.reset()
  
          class Model(torch.nn.Module):
              def __init__(self):
                  super().__init__()
                  self.fc1 = torch.nn.Linear(4, 2, bias=False)
  
              def forward(self, x):
                  return x @ self.fc1.weight.transpose(0, 1)
  
          with torch.device(device):
              model = Model()
  
              if device == "lazy":
                  if reset_storage:
                      torch._C._unsafe_reset_storage(model.fc1.weight)
  
                  torch._lazy.mark_step()
  
                  sync_tensors = metrics.counter_value(
                      "SyncedTensorsWithIR"
                  )
                  if reset_storage:
                      assert sync_tensors == 1
                  else:
                      # There is an extra tensor being unnecessarily synced if
                      # the functional storage is not reset.
                      assert sync_tensors == 2
  
              x = torch.ones(4)
              out = model(x)
  
              if device == "lazy":
                  torch._lazy.mark_step()
  
              return out
  
      cpu_out = f("cpu")
      lazy_out_1 = f("lazy", reset_storage=False)
      lazy_out_2 = f("lazy", reset_storage=True)
  
      self.assertEqual(cpu_out, lazy_out_1.to("cpu"))
      self.assertEqual(cpu_out, lazy_out_2.to("cpu"))
  
imports: import torch
  import torch._lazy.metrics
  import torch._lazy.ts_backend
  from torch.testing._internal.common_utils import run_tests, TestCase
label_desc: Use image node_id 1 for calling the LazyFuncionalizationTest obj's underlying member method code with example usage: obj.test_lazy_init_with_view() and returns: out, unknown