def reset_parameters(self):
        r"""

        Description
        -----------
        Reinitialize learnable parameters.

        Note
        ----
        The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
        The LSTM module is using xavier initialization method for its weights.
        """
        gain = nn.init.calculate_gain("relu")
        if self._aggre_type == "pool":
            nn.init.xavier_uniform_(self.fc_pool.weight, gain=gain)
        if self._aggre_type == "lstm":
            self.lstm.reset_parameters()
        if self._aggre_type != "gcn":
            nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
        nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)


        Description
        -----------
        Reinitialize learnable parameters.

        Note
        ----
        The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
        The LSTM module is using xavier initialization method for its weights.
        
python/dgl/nn/pytorch/conv/sageconv.py