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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 set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_):
 """ Set the vectors for the Vocab instance from a collection of Tensors. Arguments: stoi: A dictionary of string to the index of the associated vector in the `vectors` input argument. vectors: An indexed iterable (or other structure supporting __getitem__) that given an input index, returns a FloatTensor representing the vector for the token associated with the index. For example, vector[stoi["string"]] should return the vector for "string". dim: The dimensionality of the vectors. unk_init (callback):
 by default, initialize out-of-vocabulary word vectors to zero vectors; can be any function that takes in a Tensor and returns a Tensor of the same size. Default: torch.Tensor.zero_ """

        self.vectors = torch.Tensor(len(self), dim)
        for i, token in enumerate(self.itos):
            wv_index = stoi.get(token, None)
            if wv_index is not None:
                self.vectors[i] = vectors[wv_index]
            else:
                self.vectors[i] = unk_init(self.vectors[i])