node and associated metadata.

compilation_successful: If the `must_compile` attr is false the _XlaCompile op
   can decide not to compile the clusters based on some profitability
   heuristics.  In that case `compilation_successful` is false if _XlaCompile
   chose not to compile the cluster.  If the `must_compile` attr is true then
   _XlaCompile always attempts to compile the cluster and
   `compilation_successful` is always true.
)");

  VariableInfo(const VariableInfo&) = delete;
  VariableInfo& operator=(const VariableInfo&) = delete;

  // The index of the DT_RESOURCE input to the _XlaCompile/_XlaRun operator.
  // Note that the indices can be different between _XlaCompile and _XlaRun.
  int index() const { return index_; }

  // A pointer to the resource variable.  May be null if this VariableInfo is

#include "tensorflow/compiler/jit/defs.h"

#include <atomic>

namespace tensorflow {

const char* const kXlaMustCompileAttr = "_XlaMustCompile";

const char* const kXlaCompileAttr = "_XlaCompile";

// User-provided through jit_scope APIs. Effective only when auto_jit is OFF.
const char* const kXlaScopeAttr = "_XlaScope";

// Automatically inserted by auto_jit to guide clustering results.  Effective

  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  Node* write_op = MakeWrite(root, Output(call), "write_result");
  write_op->AddAttr(kXlaHasReferenceVarsAttr, false);

  auto xla_compile = NodeWith(Op("_XlaCompile"), Attr("must_compile", false));
  auto predicated_compilation_key =
      NodeWith(Op("Switch"), Inputs(Out(0, xla_compile), Out(1, xla_compile)));
  auto xla_run =

  }

 private:
  DeviceType device_type_;
  se::Platform::Id platform_id_;

  // xla_device_metadata_ lives in the tensorflow::DeviceBase in which the
  // XlaLaunch/_XlaCompile/_XlaRun op is placed and thus does not die before the
  // XlaLaunch/_XlaCompile/_XlaRun OpKernel.
  const XlaDevice::Metadata* xla_device_metadata_;

  // pjrt_device_metadata_ lives in tensorflow::PjRtBaseDevice in which the

extern const char* const kXlaMustCompileAttr;  // "_XlaMustCompile"

// Implies auto-clustering: tagged nodes will be clustered and compiled with XLA
// on a best-effort basis.
extern const char* const kXlaCompileAttr;  // "_XlaCompile"

// Implies auto-clustering within the given scope.
extern const char* const kXlaScopeAttr;    // "_XlaScope"
extern const char* const kXlaInternalScopeAttr;  // "_XlaInternalScope"

// The id of the compiled cluster.

  const bool has_ref_vars_;

  // cannot_compile_cluster_ is set to true if XLA returns an Unimplemented
  // error when compiling the cluster this _XlaCompile is supposed to compile.
  // If `cannot_compile_cluster_` is true then we avoid compiling this cluster
  // on any future calls to _XlaCompile.
  bool cannot_compile_cluster_ TF_GUARDED_BY(cannot_compile_cluster_mu_) =
      false;

  mutex cannot_compile_cluster_mu_;
};

#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

// Replaces TF function calls marked with `_XlaCompiledKernel` with _XlaCompile
// and _XlaRun nodes (which compile and launch, respectively, the corresponding
// HLO module).
class BuildXlaOpsPass : public GraphOptimizationPass {
 public:

    RegisterXlaActivityListener(std::move(listener));
  }

  JitCompilationListener* listener() const { return listener_; }

  // Returns a test graph that will split into two XLA clusters (due to a node
  // with _XlaCompile = false).
  GraphDef GetTestGraph(const PartialTensorShape& input_shape);

  // Runs the graph using specified batch size both with and without XLA JIT

      return copy;
    }
  };

  // Runs the MarkForCompilation pass on `graph` after assigning all nodes in
  // `graph` to the CPU device.  To make testing easier, ignores device
  // registration and  _XlaCompile attributes.
  static Status MarkForCompilation(std::unique_ptr<Graph>* graph,
                                   FunctionLibraryDefinition* flib_def,
                                   Options options = Options());