class_docstr: An AutoML pipeline manager.
class_docstr_tok: ["An","AutoML","pipeline","manager","."]
docstr: Initializes a ModelSearch.
  
  Args:
    controller: A `Controller` instance.
    scheduler: A `Scheduler` instance.
docstr_tok: ["Initializes","a","ModelSearch",".","Args",":","controller",":","A","`","Controller","`","instance",".","scheduler",":","A","`","Scheduler","`","instance","."]
code: def __init__(
      self,
      controller: Controller,
      scheduler: Scheduler = InProcessScheduler(),
  ):
      """Initializes a ModelSearch.
  
      Args:
        controller: A `Controller` instance.
        scheduler: A `Scheduler` instance.
      """
  
      self._controller = controller
      self._scheduler = scheduler
  
file_path: adanet/adanet/experimental/keras/model_search.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Sequence
  from adanet.experimental.controllers.controller import Controller
  from adanet.experimental.schedulers.in_process_scheduler import InProcessScheduler
  from adanet.experimental.schedulers.scheduler import Scheduler
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new ModelSearch object from inherited base classes: object with example: obj = ModelSearch(controller, scheduler)