code: def __init__(
      self,
      in_channels=3,
      block=BasicBlock,
      layers=[3, 4, 6, 6, 3],
      strides=[2, 1, 2, 1, 1],
  ):
      self.inplanes = 32
      super(ResNet45, self).__init__()
      self.conv1 = nn.Conv2D(
          in_channels,
          32,
          kernel_size=3,
          stride=1,
          padding=1,
          weight_attr=ParamAttr(initializer=KaimingNormal()),
          bias_attr=False,
      )
      self.bn1 = nn.BatchNorm2D(32)
      self.relu = nn.ReLU()
  
      self.layer1 = self._make_layer(
          block, 32, layers[0], stride=strides[0]
      )
      self.layer2 = self._make_layer(
          block, 64, layers[1], stride=strides[1]
      )
      self.layer3 = self._make_layer(
          block, 128, layers[2], stride=strides[2]
      )
      self.layer4 = self._make_layer(
          block, 256, layers[3], stride=strides[3]
      )
      self.layer5 = self._make_layer(
          block, 512, layers[4], stride=strides[4]
      )
      self.out_channels = 512
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
label_desc: Use image node_id 1 to create a new ResNet45 object from inherited base classes: nn with example: obj = ResNet45(in_channels, block, layers, strides)