namespace tensorflow {

namespace {
using DeadnessPredicate = DeadnessAnalysis::DeadnessPredicate;
using jit::DeviceId;
using jit::DeviceSet;

// The clusters we create here are eventually lowered into an
// _XlaCompile/_XlaRun pair with a TF executor "fallback" that uses the
// PartitionedCall op to execute the cluster in the regular graph executor if
// need be.  PartitionedCall, however, reruns the entire TF graph optimization

        // expected-remark@above {{ID: 3}}
        // expected-remark@above {{Successors: {4}}}
        "tf._XlaRun"(%arg0, %arg0) : (tensor<!tf_type.string>, tensor<!tf_type.string>) -> ()
        // expected-remark@above {{ID: 0}}
        // expected-remark@above {{Successors: {1}}}
        "tf._XlaRun"(%arg1, %arg1) : (tensor<!tf_type.string>, tensor<!tf_type.string>) -> ()
        // expected-remark@above {{ID: 1}}

        &effects) {
  effects.reserve(2 * getArgs().size() + 1);
  effects.emplace_back(MemoryEffects::Write::get(),
                       ResourceEffects::_XlaRun::get());

  // Conservatively mark resource handles as read and write, as without
  // analyzing _XlaCompile, there is not sufficient information to determine
  // effects on resources.

  let summary = "XLA Compile Op. For use by the XLA JIT only.";

  let description = [{
Compiles a TensorFlow function into an XLA LocalExecutable and returns a key
that _XlaRun can use to look up the LocalExecutable and execute it.
  }];

  let arguments = (ins
    Variadic<TF_Tensor>:$constants,
    Variadic<TF_Tensor>:$args,
    Variadic<TF_ResourceTensor>:$resources,