class_docstr: Bidirectional Feature Pyramid Network, see https://arxiv.org/abs/1911.09070
  
  Args:
      in_channels (list[int]): input channels of each level which can be
          derived from the output shape of backbone by from_config.
      out_channel (int): output channel of each level.
      num_extra_levels (int): the number of extra stages added to the last level.
          default: 2
      fpn_strides (List): The stride of each level.
      num_stacks (int): the number of stacks for BiFPN, default: 1.
      use_weighted_fusion (bool): use weighted feature fusion in BiFPN, default: True.
      norm_type (string|None): the normalization type in BiFPN module. If
          norm_type is None, norm will not be used after conv and if
          norm_type is string, bn, gn, sync_bn are available. default: bn.
      norm_groups (int): if you use gn, set this param.
      act (string|None): the activation function of BiFPN.
code: def from_config(cls, cfg, input_shape):
      return {
          "in_channels": [i.channels for i in input_shape],
          "fpn_strides": [i.stride for i in input_shape],
      }
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.layers import ConvNormLayer
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the BiFPN obj's underlying member method code with example usage: obj.from_config(cls, cfg, input_shape) and returns: dict