class_docstr: Tf.keras.Sequential with HPO capabilities.
class_docstr_tok: ["Tf.keras.Sequential","with","HPO","capabilities","."]
docstr: Initialzier.
  
  :param layers: a list of layers (optional). Defults to None.
  :param name: str(optional), name of the model. Defaults to None
docstr_tok: ["Initialzier",".",":","param","layers",":","a","list","of","layers","(","optional",")",".","Defults","to","None",".",":","param","name",":","str","(","optional",")",",","name","of","the","model",".","Defaults","to","None"]
code: def __init__(self, layers=None, name=None):
      """
      Initialzier.
  
      :param layers: a list of layers (optional). Defults to None.
      :param name: str(optional), name of the model. Defaults to None
      """
      super().__init__(layers=None, name=name)
      # TODO add more flexibility for args parsing
      # self.init_args = args
      # self.init_kwargs = kwargs
      self.model_class = tf.keras.Sequential
      self.name_ = name
      self.lazylayers_ = layers if layers is not None else []
  
file_path: BigDL/python/nano/src/bigdl/nano/automl/tf/keras/Sequential.py
imports: import tensorflow
  import copy
  from bigdl.nano.automl.utils import proxy_methods
  from bigdl.nano.automl.tf.mixin import HPOMixin
  from bigdl.nano.automl.hpo.space import AutoObject
label_desc: Use image node_id 1 to create a new Sequential object from inherited base classes: HPOMixin, tf.keras with example: obj = Sequential(layers, name)