code: def __init__(
      self, input_channels, hidden_layers, nodes=7, source_nodes=20
  ):
      super(Featuremaps_to_Graph_transfer, self).__init__()
      self.pre_fea = Parameter(torch.FloatTensor(input_channels, nodes))
      self.weight = Parameter(
          torch.FloatTensor(input_channels, hidden_layers)
      )
      self.pre_fea_transfer = nn.Sequential(
          *[
              nn.Linear(source_nodes, source_nodes),
              nn.LeakyReLU(True),
              nn.Linear(source_nodes, nodes),
              nn.LeakyReLU(True),
          ]
      )
      self.reset_parameters()
  
imports: import torch
  from .None import graph
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new Featuremaps_to_Graph_transfer object from inherited base classes: nn with example: obj = Featuremaps_to_Graph_transfer(input_channels, hidden_layers, nodes, source_nodes)