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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def extract_module(self, path: str, freeze: bool = True) -> Module: """ This method can be used to load a module from the pretrained model archive. It is also used implicitly in FromParams based construction. So instead of using standard params to construct a module, you can instead load a pretrained module from the model can use the following template:: { "_pretrained": { "archive_file": "../path/to/model.tar.gz", "path": "path.to.module.in.model", "freeze": False } } If you use this feature with FromParams, take care of the following caveat: Call to initializer(self) at end of model initializer can potentially wipe the transferred parameters by reinitializing them. This can happen if you have setup initializer regex that also matches parameters of the transferred module. To safe-guard against this, you can either update your initializer regex to prevent conflicting match or add extra initializer:: [ [".*transferred_module_name.*", "prevent"]] ] Parameters path : ``str``, required Path of target module to be loaded from the model. Eg. "_textfield_embedder.token_embedder_tokens" freeze : ``bool``, optional (default=True) Whether to freeze the module parameters or not. """

        modules_dict = {path: module for path, module in self.model.named_modules()}
        module = modules_dict.get(path, None)

        if not module:
            raise ConfigurationError(f"You asked to transfer module at path {path} from "
                                     f"the model {type(self.model)}. But it's not present.")
        if not isinstance(module, Module):
            raise ConfigurationError(f"The transferred object from model {type(self.model)} at path "
                                     f"{path} is not a PyTorch Module.")

        for parameter in module.parameters(): # type: ignore
            parameter.requires_grad_(not freeze)
        return module