def boosted_trees_predict(tree_ensemble_handle, bucketized_features, logits_dimension, name=None):
    'Runs multiple additive regression ensemble predictors on input instances and\n\n  computes the logits. It is designed to be used during prediction.\n  It traverses all the trees and calculates the final score for each instance.\n\n  Args:\n    tree_ensemble_handle: A `Tensor` of type `resource`.\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 `Tensor` of type `float32`.\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_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('BoostedTreesPredict', tree_ensemble_handle=tree_ensemble_handle, 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('BoostedTreesPredict', _inputs_flat, _attrs, _result, name)
        (_result,) = _result
        return _result
    else:
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._eager_context.device_name, 'BoostedTreesPredict', name, _ctx._post_execution_callbacks, tree_ensemble_handle, bucketized_features, 'logits_dimension', logits_dimension)
            return _result
        except _core._FallbackException:
            return boosted_trees_predict_eager_fallback(tree_ensemble_handle, 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 logits. It is designed to be used during prediction.
It traverses all the trees and calculates the final score for each instance.

Args:
  tree_ensemble_handle: A `Tensor` of type `resource`.
  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 `Tensor` of type `float32`.
def boosted_trees_predict(tree_ensemble_handle, bucketized_features, logits_dimension, name=None):
    'Runs multiple additive regression ensemble predictors on input instances and\n\n  computes the logits. It is designed to be used during prediction.\n  It traverses all the trees and calculates the final score for each instance.\n\n  Args:\n    tree_ensemble_handle: A `Tensor` of type `resource`.\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 `Tensor` of type `float32`.\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_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('BoostedTreesPredict', tree_ensemble_handle=tree_ensemble_handle, 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('BoostedTreesPredict', _inputs_flat, _attrs, _result, name)
        (_result,) = _result
        return _result
    else:
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._eager_context.device_name, 'BoostedTreesPredict', name, _ctx._post_execution_callbacks, tree_ensemble_handle, bucketized_features, 'logits_dimension', logits_dimension)
            return _result
        except _core._FallbackException:
            return boosted_trees_predict_eager_fallback(tree_ensemble_handle, 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)