class_docstr: NetworkMorphismTuner is a tuner which using network morphism techniques.
  
  Attributes
  ----------
  n_classes : int
      The class number or output node number (default: ``10``)
  input_shape : tuple
      A tuple including: (input_width, input_width, input_channel)
  t_min : float
      The minimum temperature for simulated annealing. (default: ``Constant.T_MIN``)
  beta : float
      The beta in acquisition function. (default: ``Constant.BETA``)
  algorithm_name : str
      algorithm name used in the network morphism (default: ``"Bayesian"``)
  optimize_mode : str
      optimize mode "minimize" or "maximize" (default: ``"minimize"``)
  verbose : bool
      verbose to print the log (default: ``True``)
  bo : BayesianOptimizer
      The optimizer used in networkmorphsim tuner.
  max_model_size : int
      max model size to the graph (default: ``Constant.MAX_MODEL_SIZE``)
  default_model_len : int
      default model length (default: ``Constant.MODEL_LEN``)
  default_model_width : int
      default model width (default: ``Constant.MODEL_WIDTH``)
  search_space : dict
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docstr: Call the generators to generate the initial architectures for the search.
docstr_tok: ["Call","the","generators","to","generate","the","initial","architectures","for","the","search","."]
code: def init_search(self):
      """
      Call the generators to generate the initial architectures for the search.
      """
      if self.verbose:
          logger.info("Initializing search.")
      for generator in self.generators:
          graph = generator(self.n_classes, self.input_shape).generate(
              self.default_model_len, self.default_model_width
          )
          model_id = self.model_count
          self.model_count += 1
          self.training_queue.append((graph, -1, model_id))
          self.descriptors.append(graph.extract_descriptor())
  
      if self.verbose:
          logger.info("Initialization finished.")
  
imports: import logging
  import os
  from schema import Optional, Schema
  from nni import ClassArgsValidator
  from nni.tuner import Tuner
  from nni.utils import OptimizeMode, extract_scalar_reward
  from .bayesian import BayesianOptimizer
  from .nn import CnnGenerator, MlpGenerator
  from .utils import Constant
  from .graph import graph_to_json, json_to_graph
label_desc: Use image node_id 5 for calling the NetworkMorphismTuner obj's underlying member method code with example usage: obj.init_search() without return types