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 call(
      self,
      inputs,
      training=None,
      mask=None,
  ):  # pylint: disable=arguments-differ
      if training is not None:
          raise NotImplementedError()
      if mask is not None:
          raise NotImplementedError()
      outputs = (
          inputs  # handle the corner case where self.layers is empty
      )
      for layer in self.layers:
          # During each iteration, `inputs` are the inputs to `layer`, and `outputs`
          # are the outputs of `layer` applied to `inputs`. At the end of each
          # iteration `inputs` is set to `outputs` to prepare for the next layer.
          outputs = layer(inputs)
  
          # `outputs` will be the inputs to the next layer.
          inputs = outputs
      return outputs
  
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 3 for calling the Sequential obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: outputs