Update FitTransformRegressor for changes in GraphModel

               weight_init_stddevs=[.02], bias_init_consts=[1.], penalty=0.0,

               penalty_type="l2", dropouts=[0.5], learning_rate=.001,

               momentum=.9, optimizer="adam", batch_size=50, n_classes=2,

               fit_transformers=[], verbose=True, seed=None, **kwargs):

               fit_transformers=[], n_random_samples=10, verbose=True, seed=None, **kwargs):

    self.pad_batches = False

    self.fit_transformers = fit_transformers

    self.n_random_samples = n_random_samples

    # Run fit transformers on dummy dataset to determine n_features after transformation

    # JSG This could be generalized by passing in init_data_shape rather than n_features

    # JSG for now this only works with full CoulombMatrix featurizer

    X_b = np.random.rand(batch_size, n_features, n_features)

    for transformer in self.fit_transformers:

      X_b = transformer.X_transform(X_b)

    print(X_b.shape) 

    n_features = X_b.shape[1]

    print("n_features after fit_transform: %d" % int(n_features))

          self.verbose)

    ############################################################## TIMING

  def predict_on_batch(self, X, pad_batch=False):

  def predict_on_batch(self, X):

    """Return model output for the provided input.

    Restore(checkpoint) must have previously been called on this object.