class_docstr: The base class for generating a network.
  It can be used to generate a CNN or Multi-Layer Perceptron.
  Attributes:
      n_output_node: Number of output nodes in the network.
      input_shape: A tuple to represent the input shape.
class_docstr_tok: ["The","base","class","for","generating","a","network",".","It","can","be","used","to","generate","a","CNN","or","Multi-Layer","Perceptron",".","Attributes",":","n_output_node",":","Number","of","output","nodes","in","the","network",".","input_shape",":","A","tuple","to","represent","the","input","shape","."]
code: def generate(self, model_len, model_width):
      pass
  
file_path: nni/nni/algorithms/hpo/networkmorphism_tuner/nn.py
imports: from abc import abstractmethod
  from .graph import Graph
  from .layers import StubDense, StubDropout1d, StubReLU, get_batch_norm_class, get_conv_class, get_dropout_class, get_global_avg_pooling_class, get_pooling_class
  from .utils import Constant
label_desc: Use image node_id 2 for calling the NetworkGenerator obj's underlying member method code with example usage: obj.generate(model_len, model_width) without return types