def boosted_trees_training_predict(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, logits_dimension, name=None):
    'Runs multiple additive regression ensemble predictors on input instances and\n\n  computes the update to cached logits. It is designed to be used during training.\n  It traverses the trees starting from cached tree id and cached node id and\n  calculates the updates to be pushed to the cache.\n\n  Args:\n    tree_ensemble_handle: A `Tensor` of type `resource`.\n    cached_tree_ids: A `Tensor` of type `int32`.\n      Rank 1 Tensor containing cached tree ids which is the starting\n      tree of prediction.\n    cached_node_ids: A `Tensor` of type `int32`.\n      Rank 1 Tensor containing cached node id which is the starting\n      node of prediction.\n    bucketized_features: A list of at least 1 `Tensor` objects with type `int32`.\n      A list of rank 1 Tensors containing bucket id for each\n      feature.\n    logits_dimension: An `int`.\n      scalar, dimension of the logits, to be used for partial logits\n      shape.\n    name: A name for the operation (optional).\n\n  Returns:\n    A tuple of `Tensor` objects (partial_logits, tree_ids, node_ids).\n\n    partial_logits: A `Tensor` of type `float32`.\n    tree_ids: A `Tensor` of type `int32`.\n    node_ids: A `Tensor` of type `int32`.\n  '
    _ctx = _context._context
    if ((_ctx is None) or (not _ctx._eager_context.is_eager)):
        if (not isinstance(bucketized_features, (list, tuple))):
            raise TypeError(("Expected list for 'bucketized_features' argument to 'boosted_trees_training_predict' Op, not %r." % bucketized_features))
        _attr_num_bucketized_features = len(bucketized_features)
        logits_dimension = _execute.make_int(logits_dimension, 'logits_dimension')
        (_, _, _op) = _op_def_lib._apply_op_helper('BoostedTreesTrainingPredict', tree_ensemble_handle=tree_ensemble_handle, cached_tree_ids=cached_tree_ids, cached_node_ids=cached_node_ids, bucketized_features=bucketized_features, logits_dimension=logits_dimension, name=name)
        _result = _op.outputs[:]
        _inputs_flat = _op.inputs
        _attrs = ('num_bucketized_features', _op.get_attr('num_bucketized_features'), 'logits_dimension', _op.get_attr('logits_dimension'))
        _execute.record_gradient('BoostedTreesTrainingPredict', _inputs_flat, _attrs, _result, name)
        _result = _BoostedTreesTrainingPredictOutput._make(_result)
        return _result
    else:
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._eager_context.device_name, 'BoostedTreesTrainingPredict', name, _ctx._post_execution_callbacks, tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, 'logits_dimension', logits_dimension)
            _result = _BoostedTreesTrainingPredictOutput._make(_result)
            return _result
        except _core._FallbackException:
            return boosted_trees_training_predict_eager_fallback(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, logits_dimension=logits_dimension, name=name, ctx=_ctx)
        except _core._NotOkStatusException as e:
            if (name is not None):
                message = ((e.message + ' name: ') + name)
            else:
                message = e.message
            _six.raise_from(_core._status_to_exception(e.code, message), None)
Runs multiple additive regression ensemble predictors on input instances and

computes the update to cached logits. It is designed to be used during training.
It traverses the trees starting from cached tree id and cached node id and
calculates the updates to be pushed to the cache.

Args:
  tree_ensemble_handle: A `Tensor` of type `resource`.
  cached_tree_ids: A `Tensor` of type `int32`.
    Rank 1 Tensor containing cached tree ids which is the starting
    tree of prediction.
  cached_node_ids: A `Tensor` of type `int32`.
    Rank 1 Tensor containing cached node id which is the starting
    node of prediction.
  bucketized_features: A list of at least 1 `Tensor` objects with type `int32`.
    A list of rank 1 Tensors containing bucket id for each
    feature.
  logits_dimension: An `int`.
    scalar, dimension of the logits, to be used for partial logits
    shape.
  name: A name for the operation (optional).

Returns:
  A tuple of `Tensor` objects (partial_logits, tree_ids, node_ids).

  partial_logits: A `Tensor` of type `float32`.
  tree_ids: A `Tensor` of type `int32`.
  node_ids: A `Tensor` of type `int32`.
def boosted_trees_training_predict(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, logits_dimension, name=None):
    'Runs multiple additive regression ensemble predictors on input instances and\n\n  computes the update to cached logits. It is designed to be used during training.\n  It traverses the trees starting from cached tree id and cached node id and\n  calculates the updates to be pushed to the cache.\n\n  Args:\n    tree_ensemble_handle: A `Tensor` of type `resource`.\n    cached_tree_ids: A `Tensor` of type `int32`.\n      Rank 1 Tensor containing cached tree ids which is the starting\n      tree of prediction.\n    cached_node_ids: A `Tensor` of type `int32`.\n      Rank 1 Tensor containing cached node id which is the starting\n      node of prediction.\n    bucketized_features: A list of at least 1 `Tensor` objects with type `int32`.\n      A list of rank 1 Tensors containing bucket id for each\n      feature.\n    logits_dimension: An `int`.\n      scalar, dimension of the logits, to be used for partial logits\n      shape.\n    name: A name for the operation (optional).\n\n  Returns:\n    A tuple of `Tensor` objects (partial_logits, tree_ids, node_ids).\n\n    partial_logits: A `Tensor` of type `float32`.\n    tree_ids: A `Tensor` of type `int32`.\n    node_ids: A `Tensor` of type `int32`.\n  '
    _ctx = _context._context
    if ((_ctx is None) or (not _ctx._eager_context.is_eager)):
        if (not isinstance(bucketized_features, (list, tuple))):
            raise TypeError(("Expected list for 'bucketized_features' argument to 'boosted_trees_training_predict' Op, not %r." % bucketized_features))
        _attr_num_bucketized_features = len(bucketized_features)
        logits_dimension = _execute.make_int(logits_dimension, 'logits_dimension')
        (_, _, _op) = _op_def_lib._apply_op_helper('BoostedTreesTrainingPredict', tree_ensemble_handle=tree_ensemble_handle, cached_tree_ids=cached_tree_ids, cached_node_ids=cached_node_ids, bucketized_features=bucketized_features, logits_dimension=logits_dimension, name=name)
        _result = _op.outputs[:]
        _inputs_flat = _op.inputs
        _attrs = ('num_bucketized_features', _op.get_attr('num_bucketized_features'), 'logits_dimension', _op.get_attr('logits_dimension'))
        _execute.record_gradient('BoostedTreesTrainingPredict', _inputs_flat, _attrs, _result, name)
        _result = _BoostedTreesTrainingPredictOutput._make(_result)
        return _result
    else:
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._eager_context.device_name, 'BoostedTreesTrainingPredict', name, _ctx._post_execution_callbacks, tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, 'logits_dimension', logits_dimension)
            _result = _BoostedTreesTrainingPredictOutput._make(_result)
            return _result
        except _core._FallbackException:
            return boosted_trees_training_predict_eager_fallback(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, logits_dimension=logits_dimension, name=name, ctx=_ctx)
        except _core._NotOkStatusException as e:
            if (name is not None):
                message = ((e.message + ' name: ') + name)
            else:
                message = e.message
            _six.raise_from(_core._status_to_exception(e.code, message), None)