message: add test case always predict [0, 1]
diff: @@ -18,10 +18,14 @@ from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  +from google.protobuf.json_format import ParseDict
  +from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto
  from tensorflow.contrib.tensor_forest.python import tensor_forest
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  +from tensorflow.python.ops import resources
  +from tensorflow.python.ops import variables
  from tensorflow.python.platform import googletest
  @@ -110,6 +114,30 @@ class TensorForestTest(test_util.TensorFlowTestCase):
  self.assertTrue(isinstance(paths, ops.Tensor))
  self.assertTrue(isinstance(var, ops.Tensor))
  + def testInfrenceWithPreTrainedParams(self):
  + input_data = [[-1., 0.], [-1., 2.], # node 1
  + [1., 0.], [1., -2.]] # node 2
  + expected_prediction = [[0.0, 1.0], [0.0, 1.0],
  + [0.0, 1.0], [0.0, 1.0]]
  + hparams = tensor_forest.ForestHParams(
  + num_classes=2,
  + num_features=2,
  + num_trees=1,
  + max_nodes=1000,
  + split_after_samples=25).fill()
  + tree_weight = {'decisionTree': {'nodes': [{'binaryNode': {'rightChildId': 2, 'leftChildId': 1, 'inequalityLeftChildTest': {'featureId': {'id': '0'}, 'threshold': {'floatValue': 0}}}}, {'leaf': {'vector': {'value': [{'floatValue': 0.0}, {'floatValue': 1.0}]}}, 'nodeId': 1}, {'leaf': {'vector': {'value': [{'floatValue': 0.0}, {'floatValue': 1.0}]}}, 'nodeId': 2}]}}
  + tree_param = ParseDict(tree_weight, _tree_proto.Model()).SerializeToString()
  + graph_builder = tensor_forest.RandomForestGraphs(hparams, [tree_param])
  + probs, paths, var = graph_builder.inference_graph(input_data)
  + self.assertTrue(isinstance(probs, ops.Tensor))
  + self.assertTrue(isinstance(paths, ops.Tensor))
  + self.assertTrue(isinstance(var, ops.Tensor))
  + with self.test_session():
  + variables.global_variables_initializer().run()
  + resources.initialize_resources(resources.shared_resources()).run()
  + self.assertEquals(probs.eval().shape, (4, 2))
  + self.assertEquals(probs.eval().tolist(), expected_prediction)
  +
  def testTrainingConstructionClassificationSparse(self):
  input_data = sparse_tensor.SparseTensor(
  indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]],
  