text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  Original C++ source file: gen_training_ops_py.cc
  """
  
  import collections as _collections
  import six as _six
  
  from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
  from tensorflow.python.eager import context as _context
  from tensorflow.python.eager import core as _core
  from tensorflow.python.eager import execute as _execute
  from tensorflow.python.framework import dtypes as _dtypes
  from tensorflow.python.framework import errors as _errors
  from tensorflow.python.framework import tensor_shape as _tensor_shape
  
  from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
  # Needed to trigger the call to _set_call_cpp_shape_fn.
  from tensorflow.python.framework import common_shapes as _common_shapes
  from tensorflow.python.framework import op_def_registry as _op_def_registry
  from tensorflow.python.framework import ops as _ops
  from tensorflow.python.framework import op_def_library as _op_def_library
  from tensorflow.python.util.deprecation import deprecated_endpoints
  from tensorflow.python.util import dispatch as _dispatch
  from tensorflow.python.util.tf_export import tf_export
  from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
  from tensorflow.tools.docs import doc_controls as _doc_controls
  
  
  @_dispatch.add_dispatch_list
  @tf_export('center_tree_ensemble_bias')
  def center_tree_ensemble_bias(tree_ensemble_handle, stamp_token, next_stamp_token, delta_updates, learner_config, centering_epsilon=0.01, name=None):
    r"""Centers the tree ensemble bias before adding trees based on feature splits.
  
    Args:
      tree_ensemble_handle: A `Tensor` of type `resource`.
        Handle to the ensemble variable.
      stamp_token: A `Tensor` of type `int64`.
        Stamp token for validating operation consistency.
      next_stamp_token: A `Tensor` of type `int64`.
        Stamp token to be used for the next iteration.
      delta_updates: A `Tensor` of type `float32`.
        Rank 1 Tensor containing delta updates per bias dimension.
      learner_config: A `string`.
        Config for the learner of type LearnerConfig proto.
      centering_epsilon: An optional `float`. Defaults to `0.01`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
      Scalar indicating whether more centering is needed.
    """
    _ctx = _context._context or _context.context()
    if _ctx is not None and _ctx._thread_local_data.is_eager:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._thread_local_data.device_name,
          "CenterTreeEnsembleBias", name, _ctx.post_execution_callbacks,
          tree_ensemble_handle, stamp_token, next_stamp_token, delta_updates,
          "learner_config", learner_config, "centering_epsilon",
          centering_epsilon)
        return _result
      except _core._FallbackException:
        try:
          return center_tree_ensemble_bias_eager_fallback(
              tree_ensemble_handle, stamp_token, next_stamp_token,
              delta_updates, learner_config=learner_config,
              centering_epsilon=centering_epsilon, name=name, ctx=_ctx)
        except _core._SymbolicException:
          pass  # Add nodes to the TensorFlow graph.
        except (TypeError, ValueError):
          result = _dispatch.dispatch(
                center_tree_ensemble_bias, tree_ensemble_handle=tree_ensemble_handle,
                                           stamp_token=stamp_token,
                                           next_stamp_token=next_stamp_token,
                                           delta_updates=delta_updates,
                                           learner_config=learner_config,
                                           centering_epsilon=centering_epsilon,
                                           name=name)
          if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
            return result
          raise
      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)
    # Add nodes to the TensorFlow graph.
    learner_config = _execute.make_str(learner_config, "learner_config")
    if centering_epsilon is None:
      centering_epsilon = 0.01
    centering_epsilon = _execute.make_float(centering_epsilon, "centering_epsilon")
    try:
      _, _, _op = _op_def_lib._apply_op_helper(
          "CenterTreeEnsembleBias", tree_ensemble_handle=tree_ensemble_handle,
                                    stamp_token=stamp_token,
                                    next_stamp_token=next_stamp_token,
                                    delta_updates=delta_updates,
                                    learner_config=learner_config,
                                    centering_epsilon=centering_epsilon,
                                    name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            center_tree_ensemble_bias, tree_ensemble_handle=tree_ensemble_handle,
                                       stamp_token=stamp_token,
                                       next_stamp_token=next_stamp_token,
                                       delta_updates=delta_updates,
                                       learner_config=learner_config,
                                       centering_epsilon=centering_epsilon,
                                       name=name)
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("learner_config", _op.get_attr("learner_config"),
              "centering_epsilon", _op.get_attr("centering_epsilon"))
    _execute.record_gradient(
        "CenterTreeEnsembleBias", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
  
  def CenterTreeEnsembleBias(tree_ensemble_handle, stamp_token, next_stamp_token, delta_updates, learner_config, centering_epsilon=0.01, name=None):
    return center_tree_ensemble_bias(tree_ensemble_handle=tree_ensemble_handle, stamp_token=stamp_token, next_stamp_token=next_stamp_token, delta_updates=delta_updates, learner_config=learner_config, centering_epsilon=centering_epsilon, name=name)
  CenterTreeEnsembleBias.__doc__ = center_tree_ensemble_bias.__doc__
  CenterTreeEnsembleBias = _doc_controls.do_not_generate_docs(_kwarg_only(CenterTreeEnsembleBias))
  tf_export("raw_ops.CenterTreeEnsembleBias")(CenterTreeEnsembleBias)
  
  
  def center_tree_ensemble_bias_eager_fallback(tree_ensemble_handle, stamp_token, next_stamp_token, delta_updates, learner_config, centering_epsilon=0.01, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function center_tree_ensemble_bias
    """
    _ctx = ctx if ctx else _context.context()
    learner_config = _execute.make_str(learner_config, "learner_config")
    if centering_epsilon is None:
      centering_epsilon = 0.01
    centering_epsilon = _execute.make_float(centering_epsilon, "centering_epsilon")
    tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
    stamp_token = _ops.convert_to_tensor(stamp_token, _dtypes.int64)
    next_stamp_token = _ops.convert_to_tensor(next_stamp_token, _dtypes.int64)
    delta_updates = _ops.convert_to_tensor(delta_updates, _dtypes.float32)
    _inputs_flat = [tree_ensemble_handle, stamp_token, next_stamp_token, delta_updates]
    _attrs = ("learner_config", learner_config, "centering_epsilon",
    centering_epsilon)
    _result = _execute.execute(b"CenterTreeEnsembleBias", 1,
                               inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                               name=name)
    _execute.record_gradient(
        "CenterTreeEnsembleBias", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
  
  _ops.RegisterShape("CenterTreeEnsembleBias")(None)
  
  
  @_dispatch.add_dispatch_list
  @tf_export('grow_tree_ensemble')
  def grow_tree_ensemble(tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type, partition_ids, gains, splits, learner_config, center_bias, name=None):
    r"""Grows the tree ensemble by either adding a layer to the last tree being grown
  
    or by starting a new tree.
  
    Args:
      tree_ensemble_handle: A `Tensor` of type `resource`.
        Handle to the ensemble variable.
      stamp_token: A `Tensor` of type `int64`.
        Stamp token for validating operation consistency.
      next_stamp_token: A `Tensor` of type `int64`.
        Stamp token to be used for the next iteration.
      learning_rate: A `Tensor` of type `float32`. Scalar learning rate.
      dropout_seed: A `Tensor` of type `int64`.
      max_tree_depth: A `Tensor` of type `int32`.
      weak_learner_type: A `Tensor` of type `int32`.
        The type of weak learner to use.
      partition_ids: A list of `Tensor` objects with type `int32`.
        List of Rank 1 Tensors containing partition Id per candidate.
      gains: A list with the same length as `partition_ids` of `Tensor` objects with type `float32`.
        List of Rank 1 Tensors containing gains per candidate.
      splits: A list with the same length as `partition_ids` of `Tensor` objects with type `string`.
        List of Rank 1 Tensors containing serialized SplitInfo protos per candidate.
      learner_config: A `string`.
        Config for the learner of type LearnerConfig proto.
      center_bias: A `bool`.
      name: A name for the operation (optional).
  
    Returns:
      The created Operation.
    """
    _ctx = _context._context or _context.context()
    if _ctx is not None and _ctx._thread_local_data.is_eager:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._thread_local_data.device_name,
          "GrowTreeEnsemble", name, _ctx.post_execution_callbacks,
          tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate,
          dropout_seed, max_tree_depth, weak_learner_type, partition_ids, gains,
          splits, "learner_config", learner_config, "center_bias", center_bias)
        return _result
      except _core._FallbackException:
        try:
          return grow_tree_ensemble_eager_fallback(
              tree_ensemble_handle, stamp_token, next_stamp_token,
              learning_rate, dropout_seed, max_tree_depth, weak_learner_type,
              partition_ids, gains, splits, learner_config=learner_config,
              center_bias=center_bias, name=name, ctx=_ctx)
        except _core._SymbolicException:
          pass  # Add nodes to the TensorFlow graph.
        except (TypeError, ValueError):
          result = _dispatch.dispatch(
                grow_tree_ensemble, tree_ensemble_handle=tree_ensemble_handle,
                                    stamp_token=stamp_token,
                                    next_stamp_token=next_stamp_token,
                                    learning_rate=learning_rate,
                                    dropout_seed=dropout_seed,
                                    max_tree_depth=max_tree_depth,
                                    weak_learner_type=weak_learner_type,
                                    partition_ids=partition_ids, gains=gains,
                                    splits=splits,
                                    learner_config=learner_config,
                                    center_bias=center_bias, name=name)
          if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
            return result
          raise
      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)
    # Add nodes to the TensorFlow graph.
    if not isinstance(partition_ids, (list, tuple)):
      raise TypeError(
          "Expected list for 'partition_ids' argument to "
          "'grow_tree_ensemble' Op, not %r." % partition_ids)
    _attr_num_handlers = len(partition_ids)
    if not isinstance(gains, (list, tuple)):
      raise TypeError(
          "Expected list for 'gains' argument to "
          "'grow_tree_ensemble' Op, not %r." % gains)
    if len(gains) != _attr_num_handlers:
      raise ValueError(
          "List argument 'gains' to 'grow_tree_ensemble' Op with length %d "
          "must match length %d of argument 'partition_ids'." %
          (len(gains), _attr_num_handlers))
    if not isinstance(splits, (list, tuple)):
      raise TypeError(
          "Expected list for 'splits' argument to "
          "'grow_tree_ensemble' Op, not %r." % splits)
    if len(splits) != _attr_num_handlers:
      raise ValueError(
          "List argument 'splits' to 'grow_tree_ensemble' Op with length %d "
          "must match length %d of argument 'partition_ids'." %
          (len(splits), _attr_num_handlers))
    learner_config = _execute.make_str(learner_config, "learner_config")
    center_bias = _execute.make_bool(center_bias, "center_bias")
    try:
      _, _, _op = _op_def_lib._apply_op_helper(
          "GrowTreeEnsemble", tree_ensemble_handle=tree_ensemble_handle,
                              stamp_token=stamp_token,
                              next_stamp_token=next_stamp_token,
                              learning_rate=learning_rate,
                              dropout_seed=dropout_seed,
                              max_tree_depth=max_tree_depth,
                              weak_learner_type=weak_learner_type,
                              partition_ids=partition_ids, gains=gains,
                              splits=splits, learner_config=learner_config,
                              center_bias=center_bias, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            grow_tree_ensemble, tree_ensemble_handle=tree_ensemble_handle,
                                stamp_token=stamp_token,
                                next_stamp_token=next_stamp_token,
                                learning_rate=learning_rate,
                                dropout_seed=dropout_seed,
                                max_tree_depth=max_tree_depth,
                                weak_learner_type=weak_learner_type,
                                partition_ids=partition_ids, gains=gains,
                                splits=splits, learner_config=learner_config,
                                center_bias=center_bias, name=name)
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    return _op
    _result = None
    return _result
  
  def GrowTreeEnsemble(tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type, partition_ids, gains, splits, learner_config, center_bias, name=None):
    return grow_tree_ensemble(tree_ensemble_handle=tree_ensemble_handle, stamp_token=stamp_token, next_stamp_token=next_stamp_token, learning_rate=learning_rate, dropout_seed=dropout_seed, max_tree_depth=max_tree_depth, weak_learner_type=weak_learner_type, partition_ids=partition_ids, gains=gains, splits=splits, learner_config=learner_config, center_bias=center_bias, name=name)
  GrowTreeEnsemble.__doc__ = grow_tree_ensemble.__doc__
  GrowTreeEnsemble = _doc_controls.do_not_generate_docs(_kwarg_only(GrowTreeEnsemble))
  tf_export("raw_ops.GrowTreeEnsemble")(GrowTreeEnsemble)
  
  
  def grow_tree_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type, partition_ids, gains, splits, learner_config, center_bias, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function grow_tree_ensemble
    """
    _ctx = ctx if ctx else _context.context()
    if not isinstance(partition_ids, (list, tuple)):
      raise TypeError(
          "Expected list for 'partition_ids' argument to "
          "'grow_tree_ensemble' Op, not %r." % partition_ids)
    _attr_num_handlers = len(partition_ids)
    if not isinstance(gains, (list, tuple)):
      raise TypeError(
          "Expected list for 'gains' argument to "
          "'grow_tree_ensemble' Op, not %r." % gains)
    if len(gains) != _attr_num_handlers:
      raise ValueError(
          "List argument 'gains' to 'grow_tree_ensemble' Op with length %d "
          "must match length %d of argument 'partition_ids'." %
          (len(gains), _attr_num_handlers))
    if not isinstance(splits, (list, tuple)):
      raise TypeError(
          "Expected list for 'splits' argument to "
          "'grow_tree_ensemble' Op, not %r." % splits)
    if len(splits) != _attr_num_handlers:
      raise ValueError(
          "List argument 'splits' to 'grow_tree_ensemble' Op with length %d "
          "must match length %d of argument 'partition_ids'." %
          (len(splits), _attr_num_handlers))
    learner_config = _execute.make_str(learner_config, "learner_config")
    center_bias = _execute.make_bool(center_bias, "center_bias")
    tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
    stamp_token = _ops.convert_to_tensor(stamp_token, _dtypes.int64)
    next_stamp_token = _ops.convert_to_tensor(next_stamp_token, _dtypes.int64)
    learning_rate = _ops.convert_to_tensor(learning_rate, _dtypes.float32)
    dropout_seed = _ops.convert_to_tensor(dropout_seed, _dtypes.int64)
    max_tree_depth = _ops.convert_to_tensor(max_tree_depth, _dtypes.int32)
    weak_learner_type = _ops.convert_to_tensor(weak_learner_type, _dtypes.int32)
    partition_ids = _ops.convert_n_to_tensor(partition_ids, _dtypes.int32)
    gains = _ops.convert_n_to_tensor(gains, _dtypes.float32)
    splits = _ops.convert_n_to_tensor(splits, _dtypes.string)
    _inputs_flat = [tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type] + list(partition_ids) + list(gains) + list(splits)
    _attrs = ("learner_config", learner_config, "num_handlers",
    _attr_num_handlers, "center_bias", center_bias)
    _result = _execute.execute(b"GrowTreeEnsemble", 0, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _result = None
    return _result
  
  _ops.RegisterShape("GrowTreeEnsemble")(None)
  
  
  _tree_ensemble_stats_outputs = ["num_trees", "num_layers", "active_tree",
                                 "active_layer", "attempted_trees",
                                 "attempted_layers"]
  _TreeEnsembleStatsOutput = _collections.namedtuple(
      "TreeEnsembleStats", _tree_ensemble_stats_outputs)
  
  
  @_dispatch.add_dispatch_list
  @tf_export('tree_ensemble_stats')
  def tree_ensemble_stats(tree_ensemble_handle, stamp_token, name=None):
    r"""Retrieves stats related to the tree ensemble.
  
    Args:
      tree_ensemble_handle: A `Tensor` of type `resource`.
        Handle to the ensemble variable.
      stamp_token: A `Tensor` of type `int64`.
        Stamp token for validating operation consistency.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (num_trees, num_layers, active_tree, active_layer, attempted_trees, attempted_layers).
  
      num_trees: A `Tensor` of type `int64`. Scalar indicating the number of finalized trees in the ensemble.
      num_layers: A `Tensor` of type `int64`. Scalar indicating the number of layers in the ensemble.
      active_tree: A `Tensor` of type `int64`. Scalar indicating the active tree being trained.
      active_layer: A `Tensor` of type `int64`. Scalar indicating the active layer being trained.
      attempted_trees: A `Tensor` of type `int64`.
      attempted_layers: A `Tensor` of type `int64`.
    """
    _ctx = _context._context or _context.context()
    if _ctx is not None and _ctx._thread_local_data.is_eager:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._thread_local_data.device_name,
          "TreeEnsembleStats", name, _ctx.post_execution_callbacks,
          tree_ensemble_handle, stamp_token)
        _result = _TreeEnsembleStatsOutput._make(_result)
        return _result
      except _core._FallbackException:
        try:
          return tree_ensemble_stats_eager_fallback(
              tree_ensemble_handle, stamp_token, name=name, ctx=_ctx)
        except _core._SymbolicException:
          pass  # Add nodes to the TensorFlow graph.
        except (TypeError, ValueError):
          result = _dispatch.dispatch(
                tree_ensemble_stats, tree_ensemble_handle=tree_ensemble_handle,
                                     stamp_token=stamp_token, name=name)
          if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
            return result
          raise
      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)
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op = _op_def_lib._apply_op_helper(
          "TreeEnsembleStats", tree_ensemble_handle=tree_ensemble_handle,
                               stamp_token=stamp_token, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            tree_ensemble_stats, tree_ensemble_handle=tree_ensemble_handle,
                                 stamp_token=stamp_token, name=name)
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
        "TreeEnsembleStats", _inputs_flat, _attrs, _result, name)
    _result = _TreeEnsembleStatsOutput._make(_result)
    return _result
  
  def TreeEnsembleStats(tree_ensemble_handle, stamp_token, name=None):
    return tree_ensemble_stats(tree_ensemble_handle=tree_ensemble_handle, stamp_token=stamp_token, name=name)
  TreeEnsembleStats.__doc__ = tree_ensemble_stats.__doc__
  TreeEnsembleStats = _doc_controls.do_not_generate_docs(_kwarg_only(TreeEnsembleStats))
  tf_export("raw_ops.TreeEnsembleStats")(TreeEnsembleStats)
  
  
  def tree_ensemble_stats_eager_fallback(tree_ensemble_handle, stamp_token, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function tree_ensemble_stats
    """
    _ctx = ctx if ctx else _context.context()
    tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
    stamp_token = _ops.convert_to_tensor(stamp_token, _dtypes.int64)
    _inputs_flat = [tree_ensemble_handle, stamp_token]
    _attrs = None
    _result = _execute.execute(b"TreeEnsembleStats", 6, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "TreeEnsembleStats", _inputs_flat, _attrs, _result, name)
    _result = _TreeEnsembleStatsOutput._make(_result)
    return _result
  
  _ops.RegisterShape("TreeEnsembleStats")(None)
  
  def _InitOpDefLibrary(op_list_proto_bytes):
    op_list = _op_def_pb2.OpList()
    op_list.ParseFromString(op_list_proto_bytes)
    _op_def_registry.register_op_list(op_list)
    op_def_lib = _op_def_library.OpDefLibrary()
    op_def_lib.add_op_list(op_list)
    return op_def_lib
  # op {
  #   name: "CenterTreeEnsembleBias"
  #   input_arg {
  #     name: "tree_ensemble_handle"
  #     type: DT_RESOURCE
  #   }
  #   input_arg {
  #     name: "stamp_token"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "next_stamp_token"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "delta_updates"
  #     type: DT_FLOAT
  #   }
  #   output_arg {
  #     name: "continue_centering"
  #     type: DT_BOOL
  #   }
  #   attr {
  #     name: "learner_config"
  #     type: "string"
  #   }
  #   attr {
  #     name: "centering_epsilon"
  #     type: "float"
  #     default_value {
  #       f: 0.01
  #     }
  #   }
  #   is_stateful: true
  # }
  # op {
  #   name: "GrowTreeEnsemble"
  #   input_arg {
  #     name: "tree_ensemble_handle"
  #     type: DT_RESOURCE
  #   }
  #   input_arg {
  #     name: "stamp_token"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "next_stamp_token"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "learning_rate"
  #     type: DT_FLOAT
  #   }
  #   input_arg {
  #     name: "dropout_seed"
  #     type: DT_INT64
  #   }
  #   input_arg {
  #     name: "max_tree_depth"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "weak_learner_type"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "partition_ids"
  #     type: DT_INT32
  #     number_attr: "num_handlers"
  #   }
  #   input_arg {
  #     name: "gains"
  #     type: DT_FLOAT
  #     number_attr: "num_handlers"
  #   }
  #   input_arg {
  #     name: "splits"
  #     type: DT_STRING
  #     number_attr: "num_handlers"
  #   }
  #   attr {
  #     name: "learner_config"
  #     type: "string"
  #   }
  #   attr {
  #     name: "num_handlers"
  #     type: "int"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "center_bias"
  #     type: "bool"
  #   }
  #   is_stateful: true
  # }
  # op {
  #   name: "TreeEnsembleStats"
  #   input_arg {
  #     name: "tree_ensemble_handle"
  #     type: DT_RESOURCE
  #   }
  #   input_arg {
  #     name: "stamp_token"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "num_trees"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "num_layers"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "active_tree"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "active_layer"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "attempted_trees"
  #     type: DT_INT64
  #   }
  #   output_arg {
  #     name: "attempted_layers"
  #     type: DT_INT64
  #   }
  #   is_stateful: true
  # }
  _op_def_lib = _InitOpDefLibrary(b"\n\304\001\n\026CenterTreeEnsembleBias\022\030\n\024tree_ensemble_handle\030\024\022\017\n\013stamp_token\030\t\022\024\n\020next_stamp_token\030\t\022\021\n\rdelta_updates\030\001\032\026\n\022continue_centering\030\n\"\030\n\016learner_config\022\006string\"!\n\021centering_epsilon\022\005float\032\005%\n\327#<\210\001\001\n\300\002\n\020GrowTreeEnsemble\022\030\n\024tree_ensemble_handle\030\024\022\017\n\013stamp_token\030\t\022\024\n\020next_stamp_token\030\t\022\021\n\rlearning_rate\030\001\022\020\n\014dropout_seed\030\t\022\022\n\016max_tree_depth\030\003\022\025\n\021weak_learner_type\030\003\022\037\n\rpartition_ids\030\003*\014num_handlers\022\027\n\005gains\030\001*\014num_handlers\022\030\n\006splits\030\007*\014num_handlers\"\030\n\016learner_config\022\006string\"\025\n\014num_handlers\022\003int(\001\"\023\n\013center_bias\022\004bool\210\001\001\n\256\001\n\021TreeEnsembleStats\022\030\n\024tree_ensemble_handle\030\024\022\017\n\013stamp_token\030\t\032\r\n\tnum_trees\030\t\032\016\n\nnum_layers\030\t\032\017\n\013active_tree\030\t\032\020\n\014active_layer\030\t\032\023\n\017attempted_trees\030\t\032\024\n\020attempted_layers\030\t\210\001\001")
  