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# torch.jit.ignore

torch.jit.ignore(drop=False, **kwargs)

  This decorator indicates to the compiler that a function or method should be ignored and left as a Python function. This allows you to leave code in your model that is not yet TorchScript compatible. If called from TorchScript, ignored functions will dispatch the call to the Python interpreter. Models with ignored functions cannot be exported; use `@torch.jit.unused` instead.

  Example (using `@torch.jit.ignore` on a method):

      import torch
      import torch.nn as nn

      class MyModule(nn.Module):
          @torch.jit.ignore
          def debugger(self, x):
              import pdb
              pdb.set_trace()

          def forward(self, x):
              x += 10
              # The compiler would normally try to compile `debugger`,
              # but since it is `@ignore`d, it will be left as a call
              # to Python
              self.debugger(x)
              return x

      m = torch.jit.script(MyModule())

      # Error! The call `debugger` cannot be saved since it calls into Python
      m.save("m.pt")

  Example (using `@torch.jit.ignore(drop=True)` on a method):

      import torch
      import torch.nn as nn

      class MyModule(nn.Module):
          @torch.jit.ignore(drop=True)
          def training_method(self, x):
              import pdb
              pdb.set_trace()

          def forward(self, x):
              if self.training:
                  self.training_method(x)
              return x

      m = torch.jit.script(MyModule())

      # This is OK since `training_method` is not saved, the call is replaced
      # with a `raise`.
      m.save("m.pt")

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