text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  Original C++ source file: xla_ops_wrapper_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('xla_cluster_output')
  def xla_cluster_output(input, name=None):
    r"""Operator that connects the output of an XLA computation to other consumer graph nodes.
  
    Args:
      input: A `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _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,
          "XlaClusterOutput", name, _ctx.post_execution_callbacks, input)
        return _result
      except _core._FallbackException:
        try:
          return xla_cluster_output_eager_fallback(
              input, name=name, ctx=_ctx)
        except _core._SymbolicException:
          pass  # Add nodes to the TensorFlow graph.
        except (TypeError, ValueError):
          result = _dispatch.dispatch(
                xla_cluster_output, input=input, 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(
          "XlaClusterOutput", input=input, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            xla_cluster_output, input=input, name=name)
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("T", _op._get_attr_type("T"))
    _execute.record_gradient(
        "XlaClusterOutput", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
  
  def XlaClusterOutput(input, name=None):
    return xla_cluster_output(input=input, name=name)
  XlaClusterOutput.__doc__ = xla_cluster_output.__doc__
  XlaClusterOutput = _doc_controls.do_not_generate_docs(_kwarg_only(XlaClusterOutput))
  tf_export("raw_ops.XlaClusterOutput")(XlaClusterOutput)
  
  
  def xla_cluster_output_eager_fallback(input, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function xla_cluster_output
    """
    _ctx = ctx if ctx else _context.context()
    _attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"XlaClusterOutput", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "XlaClusterOutput", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
  
  _ops.RegisterShape("XlaClusterOutput")(None)
  
  
  @_dispatch.add_dispatch_list
  @tf_export('xla_launch')
  def xla_launch(constants, args, resources, Tresults, function, name=None):
    r"""XLA Launch Op. For use by the XLA JIT only.
  
    Args:
      constants: A list of `Tensor` objects.
      args: A list of `Tensor` objects.
      resources: A list of `Tensor` objects with type `resource`.
      Tresults: A list of `tf.DTypes`.
      function: A function decorated with @Defun.
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` objects of type `Tresults`.
    """
    _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,
          "XlaLaunch", name, _ctx.post_execution_callbacks, constants, args,
          resources, "Tresults", Tresults, "function", function)
        return _result
      except _core._FallbackException:
        try:
          return xla_launch_eager_fallback(
              constants, args, resources, Tresults=Tresults, function=function,
              name=name, ctx=_ctx)
        except _core._SymbolicException:
          pass  # Add nodes to the TensorFlow graph.
        except (TypeError, ValueError):
          result = _dispatch.dispatch(
                xla_launch, constants=constants, args=args, resources=resources,
                            Tresults=Tresults, function=function, 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(resources, (list, tuple)):
      raise TypeError(
          "Expected list for 'resources' argument to "
          "'xla_launch' Op, not %r." % resources)
    _attr_Nresources = len(resources)
    if not isinstance(Tresults, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tresults' argument to "
          "'xla_launch' Op, not %r." % Tresults)
    Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
    try:
      _, _, _op = _op_def_lib._apply_op_helper(
          "XlaLaunch", constants=constants, args=args, resources=resources,
                       Tresults=Tresults, function=function, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            xla_launch, constants=constants, args=args, resources=resources,
                        Tresults=Tresults, function=function, name=name)
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _op.outputs[:]
    if not _result:
      return _op
    _inputs_flat = _op.inputs
    _attrs = ("Tconstants", _op.get_attr("Tconstants"), "Targs",
              _op.get_attr("Targs"), "Nresources", _op.get_attr("Nresources"),
              "Tresults", _op.get_attr("Tresults"), "function",
              _op.get_attr("function"))
    _execute.record_gradient(
        "XlaLaunch", _inputs_flat, _attrs, _result, name)
    return _result
  
  def XlaLaunch(constants, args, resources, Tresults, function, name=None):
    return xla_launch(constants=constants, args=args, resources=resources, Tresults=Tresults, function=function, name=name)
  XlaLaunch.__doc__ = xla_launch.__doc__
  XlaLaunch = _doc_controls.do_not_generate_docs(_kwarg_only(XlaLaunch))
  tf_export("raw_ops.XlaLaunch")(XlaLaunch)
  
  
  def xla_launch_eager_fallback(constants, args, resources, Tresults, function, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function xla_launch
    """
    _ctx = ctx if ctx else _context.context()
    if not isinstance(resources, (list, tuple)):
      raise TypeError(
          "Expected list for 'resources' argument to "
          "'xla_launch' Op, not %r." % resources)
    _attr_Nresources = len(resources)
    if not isinstance(Tresults, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tresults' argument to "
          "'xla_launch' Op, not %r." % Tresults)
    Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
    _attr_Tconstants, constants = _execute.convert_to_mixed_eager_tensors(constants, _ctx)
    _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, _ctx)
    resources = _ops.convert_n_to_tensor(resources, _dtypes.resource)
    _inputs_flat = list(constants) + list(args) + list(resources)
    _attrs = ("Tconstants", _attr_Tconstants, "Targs", _attr_Targs,
    "Nresources", _attr_Nresources, "Tresults", Tresults, "function", function)
    _result = _execute.execute(b"XlaLaunch", len(Tresults), inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "XlaLaunch", _inputs_flat, _attrs, _result, name)
    return _result
  
  _ops.RegisterShape("XlaLaunch")(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: "XlaClusterOutput"
  #   input_arg {
  #     name: "input"
  #     type_attr: "T"
  #   }
  #   output_arg {
  #     name: "outputs"
  #     type_attr: "T"
  #   }
  #   attr {
  #     name: "T"
  #     type: "type"
  #   }
  # }
  # op {
  #   name: "XlaLaunch"
  #   input_arg {
  #     name: "constants"
  #     type_list_attr: "Tconstants"
  #   }
  #   input_arg {
  #     name: "args"
  #     type_list_attr: "Targs"
  #   }
  #   input_arg {
  #     name: "resources"
  #     type: DT_RESOURCE
  #     number_attr: "Nresources"
  #   }
  #   output_arg {
  #     name: "results"
  #     type_list_attr: "Tresults"
  #   }
  #   attr {
  #     name: "Tconstants"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Targs"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Nresources"
  #     type: "int"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Tresults"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "function"
  #     type: "func"
  #   }
  #   is_stateful: true
  # }
  _op_def_lib = _InitOpDefLibrary(b"\n7\n\020XlaClusterOutput\022\n\n\005input\"\001T\032\014\n\007outputs\"\001T\"\t\n\001T\022\004type\n\332\001\n\tXlaLaunch\022\027\n\tconstants2\nTconstants\022\r\n\004args2\005Targs\022\031\n\tresources\030\024*\nNresources\032\023\n\007results2\010Tresults\"\032\n\nTconstants\022\nlist(type)(\001\"\025\n\005Targs\022\nlist(type)(\001\"\023\n\nNresources\022\003int(\001\"\030\n\010Tresults\022\nlist(type)(\001\"\020\n\010function\022\004func\210\001\001")
  