72cc9dd414ee4befbadf9bd3e4804cb2e0908e9c,allennlp/nn/initializers.py,InitializerApplicator,from_params,#Any#Any#,86

Before Change

        
        // Construct a dictionary of available initializers from the torch.nn.init package.
        all_initializer_params = params.pop("initializers", {}).as_dict()
        instantiated_initializers = {}
        for name, initializer_params in all_initializer_params.items():
            // Just a string - corresponds to the name of an initializer.
            if isinstance(initializer_params, str):
                instantiated_initializers[name] = Initializer.by_name(initializer_params)
            else:
                initializer_type = initializer_params.pop("type")
                // This is to avoid passing by reference inside the curried function.
                // Without creating a new dict, we would pass the value of initializer_params
                // when it is called, which could be different as it is a loop variable.
                init_params = {**initializer_params}

                // pylint: disable=cell-var-from-loop
                def curried_initializer(tensor: torch.Tensor):
                    return Initializer.by_name(initializer_type)(tensor, **init_params)  // type: ignore
                // pylint: enable=cell-var-from-loop
                instantiated_initializers[name] = curried_initializer  // type: ignore
        try:
            default = instantiated_initializers.pop("default")
        except KeyError:
            default = torch.nn.init.normal

After Change

        initializers = {}
        for name, initializer_params in all_initializer_params.items():
            initializers[name] = Initializer.from_params(initializer_params)
        default = initializers.pop("default", Initializer.by_name("normal")())
        exclude_regexes = params.pop("exclude", [])
        return InitializerApplicator(initializers, default, exclude_regexes)

Instances