Initialize an Outlier Detector with Custom Prediction Arguments

Add small blurb

from daml.metrics.outlier import VAEOutlier
from daml.models.tensorflow import VAE, create_model

# instantiate an outlier detector metric
metric = VAEOutlier(create_model(VAE, dataset[0].shape))

# the training set has about 15% outliers so set the fit threshold at 85%
metric.fit(dataset, threshold_perc=85, batch_size=128, verbose=False)

# detect outliers at the 'feature' level
metric.predict(dataset, outlier_type="feature")