code: def __init__(
      self,
      nInputChannels=3,
      n_classes=7,
      os=16,
      input_channels=256,
      hidden_layers=128,
      out_channels=256,
      transfer_graph=None,
      source_classes=20,
  ):
      super(
          deeplab_xception_transfer_projection_savemem, self
      ).__init__(
          nInputChannels=nInputChannels,
          n_classes=n_classes,
          os=os,
          input_channels=input_channels,
          hidden_layers=hidden_layers,
          out_channels=out_channels,
      )
      self.source_featuremap_2_graph = gcn.Featuremaps_to_Graph(
          input_channels=input_channels,
          hidden_layers=hidden_layers,
          nodes=source_classes,
      )
      self.source_graph_conv1 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
      self.source_graph_conv2 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
      self.source_graph_conv3 = gcn.GraphConvolution(
          hidden_layers, hidden_layers
      )
      self.transpose_graph = gcn.Graph_trans(
          in_features=hidden_layers,
          out_features=hidden_layers,
          adj=transfer_graph,
          begin_nodes=source_classes,
          end_nodes=n_classes,
      )
      self.fc_graph = gcn.GraphConvolution(
          hidden_layers * 3, hidden_layers
      )
  
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_projection_savemem object from inherited base classes: deeplab_xception_transfer_basemodel_savememory with example: obj = deeplab_xception_transfer_projection_savemem(nInputChannels, n_classes, os, input_channels, hidden_layers, out_channels, transfer_graph, source_classes)