code: def __init__(
      self,
      nInputChannels=3,
      n_classes=7,
      os=16,
      input_channels=256,
      hidden_layers=128,
      out_channels=256,
  ):
      super(
          deeplab_xception_transfer_basemodel_savememory, self
      ).__init__(
          nInputChannels=nInputChannels,
          n_classes=n_classes,
          os=os,
      )
      ### source graph
  
      ### target graph
      self.target_featuremap_2_graph = gcn.Featuremaps_to_Graph(
          input_channels=input_channels,
          hidden_layers=hidden_layers,
          nodes=n_classes,
      )
      self.target_graph_conv1 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
      self.target_graph_conv2 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
      self.target_graph_conv3 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
  
      self.target_graph_2_fea = gcn.Graph_to_Featuremaps_savemem(
          input_channels=input_channels,
          output_channels=out_channels,
          hidden_layers=hidden_layers,
          nodes=n_classes,
      )
      self.target_skip_conv = nn.Sequential(
          *[
              nn.Conv2d(input_channels, input_channels, kernel_size=1),
              nn.ReLU(True),
          ]
      )
  
imports: import torch
  import torch.nn
  from torch.nn import Parameter
  import torch.nn.functional
  from collections import OrderedDict
  from .None import deeplab_xception, gcn
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new deeplab_xception_transfer_basemodel_savememory object from inherited base classes: deeplab_xception with example: obj = deeplab_xception_transfer_basemodel_savememory(nInputChannels, n_classes, os, input_channels, hidden_layers, out_channels)