// XlaLaunch nodes.
//
// xla.compile() does two main things:
// a) marks operators that make up an XLA computation with the attribute
//    _xla_compile_id=XYZ, where XYZ is a unique key.
// b) adds XlaClusterOutput nodes to represent outputs of the computation.
//    These nodes are not marked with the _xla_compile_id attribute.

#ifndef TENSORFLOW_COMPILER_JIT_ENCAPSULATE_XLA_COMPUTATIONS_PASS_H_

// Automatically inserted by auto_jit to guide clustering results.  Effective
// only when auto_jit is ON.
const char* const kXlaInternalScopeAttr = "_XlaInternalScope";

const char* const kXlaClusterIdAttr = "_xla_compile_id";

static std::atomic<bool> xla_devices_creation_required(false);

// Request XLA:GPU and XLA:CPU device creation. Deprecated, only used by XRT
// backend.

extern const char* const kXlaScopeAttr;    // "_XlaScope"
extern const char* const kXlaInternalScopeAttr;  // "_XlaInternalScope"

// The id of the compiled cluster.
extern const char* const kXlaClusterIdAttr;  // "_xla_compile_id"

[[deprecated("XLA:CPU/GPU devices are deprecated")]] void
RequestXlaDevicesCreation();

[[deprecated("XLA:CPU/GPU devices are deprecated")]] bool
XlaDevicesCreationRequired();

}  // namespace tensorflow

  *(f_lib_proto.add_function()) = test::function::XTimesTwo();

  // Construct a graph:
  //   A = Placeholder[dtype=int32]
  //   B = XTimesTwo[_tpu_replicate="cluster"](A)
  //   C = XTimesTwo[_xla_compile_id="cluster"](A)
  TF_ASSERT_OK(scope_.graph()->AddFunctionLibrary(f_lib_proto));

  Output x = Placeholder(scope_, DT_FLOAT);
  NameAttrList f;
  f.set_name("XTimesTwo");
  (*f.mutable_attr())["T"].set_type(DT_FLOAT);