class_docstr: Model defined by a stack of layers in sequence.
class_docstr_tok: ["Model","defined","by","a","stack","of","layers","in","sequence","."]
docstr: Historically, `sequential.layers` only returns layers that were added
  via `add`, and omits the auto-generated `InputLayer` that comes at the
  bottom of the stack.
docstr_tok: ["Historically",",","`","sequential.layers","`","only","returns","layers","that","were","added","via","`","add","`",",","and","omits","the","auto-generated","`","InputLayer","`","that","comes","at","the","bottom","of","the","stack","."]
code: def layers(self):
      """Historically, `sequential.layers` only returns layers that were added
      via `add`, and omits the auto-generated `InputLayer` that comes at the
      bottom of the stack."""
      layers = self._layers
      if layers and isinstance(layers[0], InputLayer):
          return layers[1:]
      return layers[:]
  
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 4 for calling the Sequential obj's underlying member method code with example usage: obj.layers() and returns: layers, layers