I'm trying to extract the class probabilities of a random forest object I have trained using PySpark. However, I do not see an example of it anywhere in the documentation, nor is it a a method of `RandomForestModel`.

How can I extract class probabilities from a `RandomForestModel` classifier in PySpark?

Here's the sample code provided in the documentation that only provides the final class (not the probability):

```
from pyspark.mllib.tree import RandomForest
from pyspark.mllib.util import MLUtils

# Load and parse the data file into an RDD of LabeledPoint.
data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model.
#  Empty categoricalFeaturesInfo indicates all features are continuous.
#  Note: Use larger numTrees in practice.
#  Setting featureSubsetStrategy="auto" lets the algorithm choose.
model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
                                     numTrees=3, featureSubsetStrategy="auto",
                                     impurity='gini', maxDepth=4, maxBins=32)

# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features)) 

```

I don't see any `model.predict_proba()` method -- what should I do??