code: def start_model(train):
      rf = H2ORandomForestEstimator(
          nfolds=3,
          ntrees=10,
          max_depth=10,
          categorical_encoding="enum_limited",
          model_id="mateusz",
      )
      rf.start(
          y="model_pred",
          x=train.names.remove("y"),
          training_frame=train,
      )
      return rf
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: start_model(train) and returns: rf