pytext/torchscript/module.py [1087:1125]:
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    def __init__(
        self,
        model: torch.jit.ScriptModule,
        right_tensorizer: ScriptTensorizer,
        left_tensorizer: ScriptTensorizer,
        right_normalizer: VectorNormalizer,
        left_normalizer: VectorNormalizer,
    ):
        super().__init__(model, right_tensorizer, left_tensorizer)
        self.right_normalizer = right_normalizer
        self.left_normalizer = left_normalizer
        log_class_usage(self.__class__)

    @torch.jit.script_method
    def _forward(
        self,
        right_inputs: ScriptBatchInput,
        left_inputs: ScriptBatchInput,
        right_dense_tensor: torch.Tensor,
        left_dense_tensor: torch.Tensor,
    ):
        right_input_tensors = self.right_tensorizer(right_inputs)
        left_input_tensors = self.left_tensorizer(left_inputs)

        if self.right_tensorizer.device != "":
            right_dense_tensor = right_dense_tensor.to(self.right_tensorizer.device)
        if self.left_tensorizer.device != "":
            left_dense_tensor = left_dense_tensor.to(self.left_tensorizer.device)

        return self.model(
            right_input_tensors,
            left_input_tensors,
            right_dense_tensor,
            left_dense_tensor,
        ).cpu()

    @torch.jit.script_method
    def forward(
        self,
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pytext/torchscript/module.py [1983:2021]:
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    def __init__(
        self,
        model: torch.jit.ScriptModule,
        right_tensorizer: ScriptTensorizer,
        left_tensorizer: ScriptTensorizer,
        right_normalizer: VectorNormalizer,
        left_normalizer: VectorNormalizer,
    ):
        super().__init__(model, right_tensorizer, left_tensorizer)
        self.right_normalizer = right_normalizer
        self.left_normalizer = left_normalizer
        log_class_usage(self.__class__)

    @torch.jit.script_method
    def _forward(
        self,
        right_inputs: ScriptBatchInput,
        left_inputs: ScriptBatchInput,
        right_dense_tensor: torch.Tensor,
        left_dense_tensor: torch.Tensor,
    ):
        right_input_tensors = self.right_tensorizer(right_inputs)
        left_input_tensors = self.left_tensorizer(left_inputs)

        if self.right_tensorizer.device != "":
            right_dense_tensor = right_dense_tensor.to(self.right_tensorizer.device)
        if self.left_tensorizer.device != "":
            left_dense_tensor = left_dense_tensor.to(self.left_tensorizer.device)

        return self.model(
            right_input_tensors,
            left_input_tensors,
            right_dense_tensor,
            left_dense_tensor,
        ).cpu()

    @torch.jit.script_method
    def forward(
        self,
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