code: def __init__(self, in_channels, kernel_list=[3, 2, 2], **kwargs):
      super(Head, self).__init__()
  
      self.conv1 = nn.Conv2D(
          in_channels=in_channels,
          out_channels=in_channels // 4,
          kernel_size=kernel_list[0],
          padding=int(kernel_list[0] // 2),
          weight_attr=ParamAttr(),
          bias_attr=False,
      )
      self.conv_bn1 = nn.BatchNorm(
          num_channels=in_channels // 4,
          param_attr=ParamAttr(
              initializer=paddle.nn.initializer.Constant(value=1.0)
          ),
          bias_attr=ParamAttr(
              initializer=paddle.nn.initializer.Constant(value=1e-4)
          ),
          act="relu",
      )
  
      self.conv2 = nn.Conv2DTranspose(
          in_channels=in_channels // 4,
          out_channels=in_channels // 4,
          kernel_size=kernel_list[1],
          stride=2,
          weight_attr=ParamAttr(
              initializer=paddle.nn.initializer.KaimingUniform()
          ),
          bias_attr=get_bias_attr(in_channels // 4),
      )
      self.conv_bn2 = nn.BatchNorm(
          num_channels=in_channels // 4,
          param_attr=ParamAttr(
              initializer=paddle.nn.initializer.Constant(value=1.0)
          ),
          bias_attr=ParamAttr(
              initializer=paddle.nn.initializer.Constant(value=1e-4)
          ),
          act="relu",
      )
      self.conv3 = nn.Conv2DTranspose(
          in_channels=in_channels // 4,
          out_channels=1,
          kernel_size=kernel_list[2],
          stride=2,
          weight_attr=ParamAttr(
              initializer=paddle.nn.initializer.KaimingUniform()
          ),
          bias_attr=get_bias_attr(in_channels // 4),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppocr.modeling.backbones.det_mobilenet_v3 import ConvBNLayer
label_desc: Use image node_id 1 to create a new Head object from inherited base classes: nn with example: obj = Head(in_channels, kernel_list)