def METHOD_NAME(self, w_name, b_name, w_init=None, b_init=None,
                       w_optim=None, b_optim=None):
    """
    Initializes the Layer Parameters for weight and bias terms for features
    Inputs :
        w_blob -- blob to contain w values
        b_blob -- blob to contain b values
        w_init -- initialization distribution for weight parameter
        b_init -- initialization distribution for bias parameter
        w_optim -- optimizer to use for w; if None, then will use no optimizer
        b_optim -- optimizer to user for b; if None, then will use no optimizer
    """
    w_init = w_init if w_init else (
        'GaussianFill', {'mean': 0.0, 'std': self.stddev}
    )
    w_optim = w_optim if w_optim else self.model.NoOptim
    b_init = b_init if b_init else (
        'UniformFill', {'min': -0.5 * self.stddev, 'max': 0.5 * self.stddev}
    )
    b_optim = b_optim if b_optim else self.model.NoOptim
    w_param = self.create_param(param_name=w_name,
                                shape=(self.output_dims, self.input_dims),
                                initializer=w_init,
                                optimizer=w_optim)
    b_param = self.create_param(param_name=b_name,
                                shape=[self.output_dims],
                                initializer=b_init,
                                optimizer=b_optim)
    return [w_param, b_param]