code: def get_feat_down(conv_feat):
      # P5 = mx.symbol.Convolution(data=conv_feat[0], kernel=(1, 1), num_filter=256, name="P5_lateral")
      P5 = conv_act_layer(
          conv_feat[0],
          "P5_lateral",
          256,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          act_type="relu",
      )
  
      # P5 2x upsampling + C4 = P4
      P5_up = mx.symbol.UpSampling(
          P5,
          scale=2,
          sample_type="nearest",
          workspace=512,
          name="P5_upsampling",
          num_args=1,
      )
      # P4_la   = mx.symbol.Convolution(data=conv_feat[1], kernel=(1, 1), num_filter=256, name="P4_lateral")
      P4_la = conv_act_layer(
          conv_feat[1],
          "P4_lateral",
          256,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          act_type="relu",
      )
      P5_clip = mx.symbol.Crop(*[P5_up, P4_la], name="P4_clip")
      P4 = mx.sym.ElementWiseSum(*[P5_clip, P4_la], name="P4_sum")
      # P4      = mx.symbol.Convolution(data=P4, kernel=(3, 3), pad=(1, 1), num_filter=256, name="P4_aggregate")
      P4 = conv_act_layer(
          P4,
          "P4_aggregate",
          256,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          act_type="relu",
      )
  
      # P4 2x upsampling + C3 = P3
      P4_up = mx.symbol.UpSampling(
          P4,
          scale=2,
          sample_type="nearest",
          workspace=512,
          name="P4_upsampling",
          num_args=1,
      )
      # P3_la   = mx.symbol.Convolution(data=conv_feat[2], kernel=(1, 1), num_filter=256, name="P3_lateral")
      P3_la = conv_act_layer(
          conv_feat[2],
          "P3_lateral",
          256,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          act_type="relu",
      )
      P4_clip = mx.symbol.Crop(*[P4_up, P3_la], name="P3_clip")
      P3 = mx.sym.ElementWiseSum(*[P4_clip, P3_la], name="P3_sum")
      # P3      = mx.symbol.Convolution(data=P3, kernel=(3, 3), pad=(1, 1), num_filter=256, name="P3_aggregate")
      P3 = conv_act_layer(
          P3,
          "P3_aggregate",
          256,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          act_type="relu",
      )
  
      return P3, P4, P5
  
file_path: insightface/detection/retinaface/rcnn/symbol/symbol_ssh.py
imports: import mxnet
  import numpy
  from rcnn.config import config
  from rcnn.PY_OP import rpn_fpn_ohem3
label_desc: Use image node_id 6 for calling a global function with example usage: get_feat_down(conv_feat) and returns: P3,  P4,  P5