docstr: This generator returns all the parameters of the net except for
  the last classification layer. Note that for each batchnorm layer,
  requires_grad is set to False in deeplab_resnet.py, therefore this function does not return
  any batchnorm parameter
docstr_tok: ["This","generator","returns","all","the","parameters","of","the","net","except","for","the","last","classification","layer",".","Note","that","for","each","batchnorm","layer",",","requires_grad","is","set","to","False","in","deeplab_resnet.py",",","therefore","this","function","does","not","return","any","batchnorm","parameter"]
code: def get_1x_lr_params_NOscale(self):
      """
      This generator returns all the parameters of the net except for
      the last classification layer. Note that for each batchnorm layer,
      requires_grad is set to False in deeplab_resnet.py, therefore this function does not return
      any batchnorm parameter
      """
      b = []
  
      b.append(self.conv1)
      b.append(self.bn1)
      b.append(self.layer1)
      b.append(self.layer2)
      b.append(self.layer3)
      b.append(self.layer4)
  
      for i in range(len(b)):
          for j in b[i].modules():
              jj = 0
              for k in j.parameters():
                  jj += 1
                  if not k.stop_gradient:
                      yield k
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 5 for calling the ResNetMulti obj's underlying member method code with example usage: obj.get_1x_lr_params_NOscale() without return types