original_docstring: Create and compile a simple linear regression model.
docstring: Create and compile a simple linear regression model.
docstring_tokens: Create and compile a simple linear regression model .
code: def create_model(my_learning_rate, feature_layer):
    """Create and compile a simple linear regression model."""
    # Most simple tf.keras models are sequential.
    model = tf.keras.models.Sequential()
  
    # Add the layer containing the feature columns to the model.
    model.add(feature_layer)
  
    # Add one linear layer to the model to yield a simple linear regressor.
    model.add(tf.keras.layers.Dense(units=1, input_shape=(1,)))
  
    # Construct the layers into a model that TensorFlow can execute.
    model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),
                  loss="mean_squared_error",
                  metrics=[tf.keras.metrics.MeanSquaredError()])
  
    return model
code_tokens: def create_model ( my_learning_rate , feature_layer ) : model = tf . keras . models . Sequential ( ) model . add ( feature_layer ) model . add ( tf . keras . layers . Dense ( units = 1 , input_shape = ( 1 , ) ) ) model . compile ( optimizer = tf . keras . optimizers . RMSprop ( lr = my_learning_rate ) , loss = "mean_squared_error" , metrics = [ tf . keras . metrics . MeanSquaredError ( ) ] ) return model
short_docstring: Create and compile a simple linear regression model.
short_docstring_tokens: Create and compile a simple linear regression model .
comment: """Create and compile a simple linear regression model."""$SEP$# Most simple tf.keras models are sequential.
  $SEP$# Add the layer containing the feature columns to the model.
  $SEP$# Add one linear layer to the model to yield a simple linear regressor.
  $SEP$# Construct the layers into a model that TensorFlow can execute.
  