class_docstr: ResNet model from
  `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
  
  Args:
      Block (BasicBlock|BottleneckBlock): Block module of model.
      depth (int, optional): Layers of resnet, Default: 50.
      width (int, optional): Base width per convolution group for each convolution block, Default: 64.
      in_channels (int, optional): The channels of input image. Default: 3.
      groups (int, optional): Number of groups for each convolution block, Default: 1.
      ssl_pretrained (bool, optinal) : Use self-supervised pretrained model or not. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def __init__(
      self,
      block,
      depth=50,
      width=64,
      groups=1,
      in_channels=3,
      ssl_pretrained=False,
      pretrained=None,
  ):
      super(ResNet, self).__init__()
      layer_cfg = {
          18: [2, 2, 2, 2],
          34: [3, 4, 6, 3],
          50: [3, 4, 6, 3],
          101: [3, 4, 23, 3],
          152: [3, 8, 36, 3],
      }
      layers = layer_cfg[depth]
      self.groups = groups
      self.base_width = width
      self._norm_layer = nn.BatchNorm2D
  
      self.inplanes = 64
      self.dilation = 1
  
      self.conv1 = nn.Conv2D(
          in_channels,
          self.inplanes,
          kernel_size=7,
          stride=2,
          padding=3,
          bias_attr=False,
      )
      self.bn1 = self._norm_layer(self.inplanes)
      self.relu = nn.ReLU()
      self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
      self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
  
      num_filters = [64, 128, 256, 512]
      self.feat_channels = [c * 4 for c in num_filters]
      self.pretrained = pretrained
      self.ssl_pretrained = ssl_pretrained
      self.init_weight()
  
imports: import os
  from urllib.parse import urlparse
  import paddle
  import paddle.nn
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(block, depth, width, groups, in_channels, ssl_pretrained, pretrained)