code: def instantiate(
      self,
      hidden_dim: _typ.Optional[int] = None,
      layer_number: _typ.Optional[int] = None,
      input_dim: _typ.Optional[int] = None,
      output_dim: _typ.Optional[int] = None,
      ops: _typ.Tuple = None,
      dropout=None,
  ):
      super().instantiate()
      self.hidden_dim = hidden_dim or self.hidden_dim
      self.layer_number = layer_number or self.layer_number
      self.input_dim = input_dim or self.input_dim
      self.output_dim = output_dim or self.output_dim
      self.ops = ops or self.ops
      self.dropout = dropout or self.dropout
  
      num_feat = self.input_dim
      num_label = self.output_dim
  
      layer_nums = self.layer_number
      state_num = 5
  
      # build hidden layer
      for i in range(layer_nums):
          # extract layer information
          setattr(
              self,
              f"attention_{i}",
              self.setLayerChoice(
                  i * state_num + 0,
                  map_nn(
                      [
                          "gat",
                          "gcn",
                          "cos",
                          "const",
                          "gat_sym",
                          "linear",
                          "generalized_linear",
                      ]
                  ),
                  key=f"attention_{i}",
              ),
          )
          setattr(
              self,
              f"aggregator_{i}",
              self.setLayerChoice(
                  i * state_num + 1,
                  map_nn(
                      [
                          "sum",
                          "mean",
                          "max",
                          "mlp",
                      ]
                  ),
                  key=f"aggregator_{i}",
              ),
          )
          setattr(
              self,
              f"act_{i}",
              self.setLayerChoice(
                  i * state_num + 0,
                  map_nn(
                      [
                          "sigmoid",
                          "tanh",
                          "relu",
                          "linear",
                          "softplus",
                          "leaky_relu",
                          "relu6",
                          "elu",
                      ]
                  ),
                  key=f"act_{i}",
              ),
          )
          setattr(
              self,
              f"head_{i}",
              self.setLayerChoice(
                  i * state_num + 0,
                  map_nn([1, 2, 4, 6, 8, 16]),
                  key=f"head_{i}",
              ),
          )
          if i < layer_nums - 1:
              setattr(
                  self,
                  f"out_channels_{i}",
                  self.setLayerChoice(
                      i * state_num + 0,
                      map_nn([4, 8, 16, 32, 64, 128, 256]),
                      key=f"out_channels_{i}",
                  ),
              )
  
imports: import torch
  import typing
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_space
  from .base import BaseSpace, map_nn
  from ...model import BaseAutoModel
  from .operation import act_map
  from ..utils import count_parameters, measure_latency
  from ..backend import *
  from operator import *
  from .operation import *
label_desc: Use image node_id 2 for calling the GraphNasMacroNodeClassificationSpace obj's underlying member method code with example usage: obj.instantiate(hidden_dim, layer_number, input_dim, output_dim, ops, dropout) without return types