class_docstr: A global configuration object for HPO.
  
  To access the hpo config, use "bigdl.nano.automl.hpo_config".
  E.g., to enable hpo tf, use "bigd.nano.automl.hpo_config.enable_hpo_tf()"
class_docstr_tok: ["A","global","configuration","object","for","HPO",".","To","access","the","hpo","config",",","use","``","bigdl.nano.automl.hpo_config","''",".","E.g.",",","to","enable","hpo","tf",",","use","``","bigd.nano.automl.hpo_config.enable_hpo_tf","(",")","''"]
docstr: Init the HPOConfig.
  
  :param hpo_tf: boolean. whether to enable HPO for Tensorflow.
      Defaults to False
  :param hpo_pytorch: boolean. whether to enable HPO for PyTorch.
      Defaults to False
docstr_tok: ["Init","the","HPOConfig",".",":","param","hpo_tf",":","boolean",".","whether","to","enable","HPO","for","Tensorflow",".","Defaults","to","False",":","param","hpo_pytorch",":","boolean",".","whether","to","enable","HPO","for","PyTorch",".","Defaults","to","False"]
code: def __init__(self, hpo_tf=False, hpo_pytorch=False):
      """
      Init the HPOConfig.
  
      :param hpo_tf: boolean. whether to enable HPO for Tensorflow.
          Defaults to False
      :param hpo_pytorch: boolean. whether to enable HPO for PyTorch.
          Defaults to False
      """
      # configuraitons
      self.hpo_tf_ = hpo_tf
      self.hpo_pytorch_ = hpo_pytorch
  
      self.torch_available = False
      try:
          import torch
  
          self.torch_available = True
      except ImportError:
          pass
      self.tf_available = False
      try:
          import tensorflow
  
          self.tf_available = True
      except ImportError:
          pass
  
      self.added_tf_activations = []
      self.added_tf_layers = []
      self.backup_tf_components = None
  
file_path: BigDL/python/nano/src/bigdl/nano/automl/hpo/config.py
imports: import importlib
  from bigdl.nano.automl.utils.register_modules import COMPONENT_TYPE, register_module, register_module_simple, clean_modules_simple
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 1 to create a new HPOConfig object from inherited base classes: object with example: obj = HPOConfig(hpo_tf, hpo_pytorch)