def __init__(self, cfg, input_shape: ShapeSpec):
        """
        The following attributes are parsed from config:
            fc_dim: the output dimension of each FC layers
            num_fc: the number of FC layers
            coarse_pred_each_layer: if True, coarse prediction features are concatenated to each
                layer's input
        """
        super(StandardPointHead, self).__init__()
        # fmt: off
        num_classes                 = cfg.MODEL.POINT_HEAD.NUM_CLASSES
        fc_dim                      = cfg.MODEL.POINT_HEAD.FC_DIM
        num_fc                      = cfg.MODEL.POINT_HEAD.NUM_FC
        cls_agnostic_mask           = cfg.MODEL.POINT_HEAD.CLS_AGNOSTIC_MASK
        self.coarse_pred_each_layer = cfg.MODEL.POINT_HEAD.COARSE_PRED_EACH_LAYER
        input_channels              = input_shape.channels
        # fmt: on

        fc_dim_in = input_channels + num_classes
        self.fc_layers = []
        for k in range(num_fc):
            fc = nn.Conv1d(fc_dim_in, fc_dim, kernel_size=1, stride=1, padding=0, bias=True)
            self.add_module("fc{}".format(k + 1), fc)
            self.fc_layers.append(fc)
            fc_dim_in = fc_dim
            fc_dim_in += num_classes if self.coarse_pred_each_layer else 0

        num_mask_classes = 1 if cls_agnostic_mask else num_classes
        self.predictor = nn.Conv1d(fc_dim_in, num_mask_classes, kernel_size=1, stride=1, padding=0)

        for layer in self.fc_layers:
            weight_init.c2_msra_fill(layer)
        # use normal distribution initialization for mask prediction layer
        nn.init.normal_(self.predictor.weight, std=0.001)
        if self.predictor.bias is not None:
            nn.init.constant_(self.predictor.bias, 0)

        The following attributes are parsed from config:
            fc_dim: the output dimension of each FC layers
            num_fc: the number of FC layers
            coarse_pred_each_layer: if True, coarse prediction features are concatenated to each
                layer's input
        
EVA-02/det/projects/PointRend/point_rend/point_head.py