code: def __init__(self, in_channels, model_name, **kwargs):
      super(EASTFPN, self).__init__()
      self.model_name = model_name
      if self.model_name == "large":
          self.out_channels = 128
      else:
          self.out_channels = 64
      self.in_channels = in_channels[::-1]
      self.h1_conv = ConvBNLayer(
          in_channels=self.out_channels + self.in_channels[1],
          out_channels=self.out_channels,
          kernel_size=3,
          stride=1,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_h_1",
      )
      self.h2_conv = ConvBNLayer(
          in_channels=self.out_channels + self.in_channels[2],
          out_channels=self.out_channels,
          kernel_size=3,
          stride=1,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_h_2",
      )
      self.h3_conv = ConvBNLayer(
          in_channels=self.out_channels + self.in_channels[3],
          out_channels=self.out_channels,
          kernel_size=3,
          stride=1,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_h_3",
      )
      self.g0_deconv = DeConvBNLayer(
          in_channels=self.in_channels[0],
          out_channels=self.out_channels,
          kernel_size=4,
          stride=2,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_g_0",
      )
      self.g1_deconv = DeConvBNLayer(
          in_channels=self.out_channels,
          out_channels=self.out_channels,
          kernel_size=4,
          stride=2,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_g_1",
      )
      self.g2_deconv = DeConvBNLayer(
          in_channels=self.out_channels,
          out_channels=self.out_channels,
          kernel_size=4,
          stride=2,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_g_2",
      )
      self.g3_conv = ConvBNLayer(
          in_channels=self.out_channels,
          out_channels=self.out_channels,
          kernel_size=3,
          stride=1,
          padding=1,
          if_act=True,
          act="relu",
          name="unet_g_3",
      )
  
file_path: PaddleOCR/ppocr/modeling/necks/east_fpn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 1 to create a new EASTFPN object from inherited base classes: nn with example: obj = EASTFPN(in_channels, model_name)