code: def buildModelCheckpointing(
      training_frame, x_indices, y_index, family, solver
  ):
      split_frames = training_frame.split_frame(
          ratios=[0.9], seed=12345
      )
      model = H2OGeneralizedLinearEstimator(
          family=family, max_iterations=3, solver=solver
      )
      model.train(
          training_frame=split_frames[0],
          x=x_indices,
          y=y_index,
          validation_frame=split_frames[1],
      )
      modelCheckpoint = H2OGeneralizedLinearEstimator(
          family=family, checkpoint=model.model_id, solver=solver
      )
      modelCheckpoint.train(
          training_frame=split_frames[0],
          x=x_indices,
          y=y_index,
          validation_frame=split_frames[1],
      )
      modelLong = H2OGeneralizedLinearEstimator(
          family=family, solver=solver
      )  # allow to run to completion
      modelLong.train(
          training_frame=split_frames[0],
          x=x_indices,
          y=y_index,
          validation_frame=split_frames[1],
      )
      checkpointCoef = modelCheckpoint.coef()
      longCoef = modelLong.coef()
      for key in longCoef.keys():
          pyunit_utils.assertEqualCoeffDicts(
              checkpointCoef[key], longCoef[key], tol=1e-6
          )
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: buildModelCheckpointing(training_frame, x_indices, y_index, family, solver) without return types