class_docstr: Path Aggregation Network with LCNet module.
  Args:
      in_channels (List[int]): Number of input channels per scale.
      out_channels (int): Number of output channels (used at each scale)
      kernel_size (int): The conv2d kernel size of this Module.
      num_features (int): Number of output features of CSPPAN module.
      num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 1
      use_depthwise (bool): Whether to depthwise separable convolution in
          blocks. Default: True
class_docstr_tok: ["Path","Aggregation","Network","with","LCNet","module",".","Args",":","in_channels","(","List","[","int","]",")",":","Number","of","input","channels","per","scale",".","out_channels","(","int",")",":","Number","of","output","channels","(","used","at","each","scale",")","kernel_size","(","int",")",":","The","conv2d","kernel","size","of","this","Module",".","num_features","(","int",")",":","Number","of","output","features","of","CSPPAN","module",".","num_csp_blocks","(","int",")",":","Number","of","bottlenecks","in","CSPLayer",".","Default",":","1","use_depthwise","(","bool",")",":","Whether","to","depthwise","separable","convolution","in","blocks",".","Default",":","True"]
code: def out_shape(self):
      return [
          ShapeSpec(channels=self.out_channels, stride=1.0 / s)
          for s in self.spatial_scales
      ]
  
file_path: PaddleDetection/ppdet/modeling/necks/lc_pan.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
  from ..backbones.lcnet import DepthwiseSeparable
  from .csp_pan import ConvBNLayer, Channel_T, DPModule
label_desc: Use image node_id 3 for calling the LCPAN obj's underlying member method code with example usage: obj.out_shape() and returns: unknown