through the output of the *E-step*.
*  Contrastive divergence training of Boltzmann machines where, when
   differentiating the energy function, the training must not backpropagate
   through the graph that generated the samples from the model.
*  Adversarial training, where no backprop should happen through the adversarial
   example generation process.
  }];

    [`tf.distribute.Strategy`](https://www.tensorflow.org/beta/guide/distribute_strategy)
    API to distribute training with minimal code changes, yielding great
    out-of-the-box performance. It supports distributed training with Keras
    model.fit, as well as with custom training loops. Multi-GPU support is
    available, along with experimental support for multi worker and Cloud TPUs.
    Check out the