new_contents: import pipeline
  
  import tensorflow as tf
  from keras.layers import Dense, Flatten, Lambda, Input, Concatenate, Add
  from keras.layers.convolutional import Conv2D
  from keras.models import Model
  from keras.optimizers import Adam
  
  nb_epochs = 10
  nb_training_samples_per_epoch = 100
  nb_validation_samples = int(0.2 * nb_training_samples_per_epoch)
  
  
  
  
  # The model is ispired by the NVIDIA paper "End to End Learning for Self-Driving Cars"
  # Paper: https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
  
  init = Input(shape=(32, 32, 3))
  x    = Lambda(lambda x: x / 127.5 - 1.0)(init)
  
  # Convolutional layers
  x    = Conv2D(16, (3,3), activation='relu', padding='same', strides=(2,2) )(x)
  x    = Conv2D(32, (3,3), activation='relu', padding='same', strides=(1,1) )(x)
  x    = Conv2D(48, (3,3), activation='relu', padding='same', strides=(1,1) )(x)
  x    = Conv2D(64, (3,3), activation='relu', padding='same', strides=(1,1) )(x)
  x    = Conv2D(64, (3,3), activation='relu', padding='same', strides=(1,1) )(x)
  x    = Conv2D(64, (3,3), activation='relu', padding='same', strides=(2,2) )(x)
  
  # Fully connected layers
  x    = Dense(100, activation='relu')(x)
  x    = Dense(10, activation='relu')(x)
  
  # Output without activation
  out  = Dense(1)(x)
  
  model = Model(init, out)
  model.summary()
  
  
subject: Add initial model