class_docstr: Model defined by a stack of layers in sequence.
class_docstr_tok: ["Model","defined","by","a","stack","of","layers","in","sequence","."]
docstr: Instantiates a TFE Keras model from its config.
  
  Arguments:
    config: Configuration dictionary matching the output of
      model.get_weights().
  
  Returns:
      A TFE Keras Sequential instance.
docstr_tok: ["Instantiates","a","TFE","Keras","model","from","its","config",".","Arguments",":","config",":","Configuration","dictionary","matching","the","output","of","model.get_weights","(",")",".","Returns",":","A","TFE","Keras","Sequential","instance","."]
code: def from_config(cls, config):
      """Instantiates a TFE Keras model from its config.
  
      Arguments:
        config: Configuration dictionary matching the output of
          model.get_weights().
  
      Returns:
          A TFE Keras Sequential instance.
      """
      tfe_model = model_from_config(config)
  
      return tfe_model
  
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 8 for calling the Sequential obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: tfe_model