class_docstr: Head block for Face detection network
  
  Args:
      num_classes (int): Number of output classes.
      in_channels (int): Number of input channels.
      anchor_generator(object): instance of anchor genertor method.
      kernel_size (int): kernel size of Conv2D in FaceHead.
      padding (int): padding of Conv2D in FaceHead.
      conv_decay (float): norm_decay (float): weight decay for conv layer weights.
      loss (object): loss of face detection model.
class_docstr_tok: ["Head","block","for","Face","detection","network","Args",":","num_classes","(","int",")",":","Number","of","output","classes",".","in_channels","(","int",")",":","Number","of","input","channels",".","anchor_generator","(","object",")",":","instance","of","anchor","genertor","method",".","kernel_size","(","int",")",":","kernel","size","of","Conv2D","in","FaceHead",".","padding","(","int",")",":","padding","of","Conv2D","in","FaceHead",".","conv_decay","(","float",")",":","norm_decay","(","float",")",":","weight","decay","for","conv","layer","weights",".","loss","(","object",")",":","loss","of","face","detection","model","."]
code: def from_config(cls, cfg, input_shape):
      return {
          "in_channels": [i.channels for i in input_shape],
      }
  
file_path: PaddleDetection/ppdet/modeling/heads/face_head.py
imports: import paddle
  import paddle.nn
  from ppdet.core.workspace import register
  from ..layers import AnchorGeneratorSSD
  from ..cls_utils import _get_class_default_kwargs
label_desc: Use image node_id 2 for calling the FaceHead obj's underlying member method code with example usage: obj.from_config(cls, cfg, input_shape) and returns: dict