class_docstr: PSANet
  Parameters
  ----------
  :param num_classes : int
      Number of categories for the training dataset.
  :param backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  :param norm_layer : object
      Normalization layer used in backbone network (default: :class:`nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  :param aux : (bool, default=False)  Whether to use auxiliary loss.
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code: def __init__(
      self,
      num_classes,
      pretrained=True,
      backbone="resnet101",
      aux=False,
      **kwargs,
  ):
      super(PSANet, self).__init__(
          num_classes,
          pretrained=pretrained,
          aux=aux,
          backbone=backbone,
          **kwargs,
      )
      self.head = _PSAHead(num_classes, **kwargs)
      if aux:
          self.auxlayer = _FCNHead(1024, num_classes, **kwargs)
  
      self.__setattr__(
          "exclusive", ["head", "auxlayer"] if aux else ["head"]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.basic import _ConvBNReLU
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new PSANet object from inherited base classes: SegBaseModel with example: obj = PSANet(num_classes, pretrained, backbone, aux)