code: def conv_act_layer(
      from_layer,
      name,
      num_filter,
      kernel=(1, 1),
      pad=(0, 0),
      stride=(1, 1),
      act_type="relu",
      bias_wd_mult=0.0,
      dcn=False,
  ):
      weight = mx.symbol.Variable(
          name="{}_weight".format(name),
          init=mx.init.Normal(0.01),
          attr={"__lr_mult__": "1.0"},
      )
      bias = mx.symbol.Variable(
          name="{}_bias".format(name),
          init=mx.init.Constant(0.0),
          attr={"__lr_mult__": "2.0", "__wd_mult__": str(bias_wd_mult)},
      )
      if not dcn:
          conv = mx.symbol.Convolution(
              data=from_layer,
              kernel=kernel,
              pad=pad,
              stride=stride,
              num_filter=num_filter,
              name="{}".format(name),
              weight=weight,
              bias=bias,
          )
      else:
          assert kernel[0] == 3 and kernel[1] == 3
          num_group = 1
          f = num_group * 18
          offset_weight = mx.symbol.Variable(
              name="{}_offset_weight".format(name),
              init=mx.init.Constant(0.0),
              attr={"__lr_mult__": "1.0"},
          )
          offset_bias = mx.symbol.Variable(
              name="{}_offset_bias".format(name),
              init=mx.init.Constant(0.0),
              attr={
                  "__lr_mult__": "2.0",
                  "__wd_mult__": str(bias_wd_mult),
              },
          )
          conv_offset = mx.symbol.Convolution(
              name=name + "_offset",
              data=from_layer,
              weight=offset_weight,
              bias=offset_bias,
              num_filter=f,
              pad=(1, 1),
              kernel=(3, 3),
              stride=(1, 1),
          )
          conv = mx.contrib.symbol.DeformableConvolution(
              name=name,
              data=from_layer,
              offset=conv_offset,
              weight=weight,
              bias=bias,
              num_filter=num_filter,
              pad=(1, 1),
              kernel=(3, 3),
              num_deformable_group=num_group,
              stride=(1, 1),
              no_bias=False,
          )
      if len(act_type) > 0:
          relu = mx.symbol.Activation(
              data=conv,
              act_type=act_type,
              name="{}_{}".format(name, act_type),
          )
      else:
          relu = conv
      return relu
  
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 1 for calling a global function with example usage: conv_act_layer(from_layer, name, num_filter, kernel, pad, stride, act_type, bias_wd_mult, dcn) and returns: relu