def reset_parameters(self):
        r"""

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

        Note
        ----
        The fc parameters are initialized using Glorot uniform initialization
        and the bias is initialized to be zero.
        The mu weight is initialized using normal distribution and
        inv_sigma is initialized with constant value 1.0.
        """
        gain = init.calculate_gain("relu")
        init.xavier_normal_(self.fc.weight, gain=gain)
        if isinstance(self.res_fc, nn.Linear):
            init.xavier_normal_(self.res_fc.weight, gain=gain)
        init.normal_(self.mu.data, 0, 0.1)
        init.constant_(self.inv_sigma.data, 1)
        if self.bias is not None:
            init.zeros_(self.bias.data)


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

        Note
        ----
        The fc parameters are initialized using Glorot uniform initialization
        and the bias is initialized to be zero.
        The mu weight is initialized using normal distribution and
        inv_sigma is initialized with constant value 1.0.
        
python/dgl/nn/pytorch/conv/gmmconv.py