class_docstr: Model defined by a stack of layers in sequence.
class_docstr_tok: ["Model","defined","by","a","stack","of","layers","in","sequence","."]
code: def __init__(self, layers=None, name=None):
      super(Sequential, self).__init__(name=name)
  
      self._layers = []
  
      # Add to the model any layers passed to the constructor.
      if layers:
          for layer in layers:
              self.add(layer)
  
file_path: tf-encrypted/tf_encrypted/keras/models/sequential.py
imports: import tensorflow
  import tf_encrypted
  from tf_encrypted.keras import optimizers
  from tf_encrypted.keras.engine import Layer
  from tf_encrypted.keras.engine.input_layer import Input
  from tf_encrypted.keras.engine.input_layer import InputLayer
  from .base_model import BaseModel
label_desc: Use image node_id 1 to create a new Sequential object from inherited base classes: BaseModel with example: obj = Sequential(layers, name)