code: def mnist_many_cols_gbm_large():
      train = h2o.import_file(
          path=pyunit_utils.locate("bigdata/laptop/mnist/train.csv.gz")
      )
      train.tail()
  
      gbm_mnist = H2OGradientBoostingEstimator(
          ntrees=1, max_depth=1, min_rows=10, learn_rate=0.01
      )
      gbm_mnist.train(x=list(range(784)), y=784, training_frame=train)
      gbm_mnist.show()
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_many_cols_gbm_large() without return types