Added simple code examples

     Keras Model class, such as uncertainty prediction and

     saliency mapping.

  Here is a simple example of code that uses KerasModel to train

  a Keras model on a DeepChem dataset.

  >> keras_model = tf.keras.Sequential([

  >>    tf.keras.layers.Dense(1000, activation='tanh'),

  >>    tf.keras.layers.Dense(1)

  >> ])

  >> model = KerasModel(keras_model, loss=dc.models.losses.L2Loss())

  >> model.fit(dataset)

  The loss function for a model can be defined in two different

  ways.  For models that have only a single output and use a

  standard loss function, you can simply provide a

class TorchModel(Model):

  """This is a DeepChem model implemented by a PyTorch model.

  Here is a simple example of code that uses TorchModel to train

  a PyTorch model on a DeepChem dataset.

  >> pytorch_model = torch.nn.Sequential(

  >>    torch.nn.Linear(100, 1000),

  >>    torch.nn.Tanh(),

  >>    torch.nn.Linear(1000, 1))

  >> model = TorchModel(pytorch_model, loss=dc.models.losses.L2Loss())

  >> model.fit(dataset)

  The loss function for a model can be defined in two different

  ways.  For models that have only a single output and use a

  standard loss function, you can simply provide a

  dc.models.losses.Loss object.  This defines the loss for each

  sample or sample/task pair.  The result is automatically

  multiplied by the weights and averaged over the batch.  Any

  additional losses computed by model layers, such as weight

  decay penalties, are also added.

  multiplied by the weights and averaged over the batch.

  For more complicated cases, you can instead provide a function

  that directly computes the total loss.  It must be of the form