How to use the mmcv.cnn.normal_init function in mmcv

def init_weights(self):
        if not self.use_dcn:
            for m in self.cls_convs:
                normal_init(m.conv, std=0.01)
            for m in self.reg_convs:
                normal_init(m.conv, std=0.01)
            for m in self.mask_convs:
                normal_init(m.conv, std=0.01)
        else:
            pass

        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.polar_cls, std=0.01, bias=bias_cls)
        normal_init(self.polar_reg, std=0.01)
        normal_init(self.polar_mask, std=0.01)
        normal_init(self.polar_centerness, std=0.01)

def init_weights(self):
        normal_init(self.conv_cls, std=0.01)
        normal_init(self.conv_reg, std=0.01)

def init_weights(self):
        for m in self.cls_convs:
            normal_init(m.conv, std=0.01)
        for m in self.reg_convs:
            normal_init(m.conv, std=0.01)
        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.reppoints_cls_conv, std=0.01)
        normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls)
        normal_init(self.reppoints_pts_init_conv, std=0.01)
        normal_init(self.reppoints_pts_init_out, std=0.01)
        normal_init(self.reppoints_pts_refine_conv, std=0.01)
        normal_init(self.reppoints_pts_refine_out, std=0.01)

def init_weights(self):
        for m in self.cls_convs:
            normal_init(m.conv, std=0.01)
        for m in self.reg_convs:
            normal_init(m.conv, std=0.01)

        self.feature_adaption_cls.init_weights()
        self.feature_adaption_reg.init_weights()

        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.conv_loc, std=0.01, bias=bias_cls)
        normal_init(self.conv_shape, std=0.01)
        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
        normal_init(self.retina_reg, std=0.01)

def init_weights(self):
        normal_init(self.conv_cls, std=0.01)
        normal_init(self.conv_reg, std=0.01)

def init_weights(self):
        normal_init(self.conv_cls, std=0.01)
        normal_init(self.conv_reg, std=0.01)

def init_weights(self):
        normal_init(self.rpn_conv, std=0.01)
        normal_init(self.rpn_cls, std=0.01)
        normal_init(self.rpn_reg, std=0.01)

def init_weights(self):
        for m in self.cls_convs:
            normal_init(m.conv, std=0.01)
        for m in self.reg_convs:
            normal_init(m.conv, std=0.01)
        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.fcos_cls, std=0.01, bias=bias_cls)
        normal_init(self.fcos_reg, std=0.01)
        normal_init(self.fcos_centerness, std=0.01)

def init_weights(self):
        for m in self.cls_convs:
            normal_init(m.conv, std=0.01)
        for m in self.reg_convs:
            normal_init(m.conv, std=0.01)

        self.feature_adaption_cls.init_weights()
        self.feature_adaption_reg.init_weights()

        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.conv_loc, std=0.01, bias=bias_cls)
        normal_init(self.conv_shape, std=0.01)
        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
        normal_init(self.retina_reg, std=0.01)

def init_weights(self):
        if not self.use_dcn:
            for m in self.cls_convs:
                normal_init(m.conv, std=0.01)
            for m in self.reg_convs:
                normal_init(m.conv, std=0.01)
            for m in self.mask_convs:
                normal_init(m.conv, std=0.01)
        else:
            pass

        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.fcos_cls, std=0.01, bias=bias_cls)
        normal_init(self.fcos_reg, std=0.01)
        normal_init(self.fcos_mask, std=0.01)
        normal_init(self.fcos_centerness, std=0.01)