err := NewFunc(nopos, nil, "Error", sig)

		// interface{ Error() string }
		ityp := &Interface{methods: []*Func{err}, complete: true}
		computeInterfaceTypeSet(nil, nopos, ityp) // prevent races due to lazy computation of tset

		typ.SetUnderlying(ityp)
		def(obj)
	}

	// type comparable interface{} // marked as comparable
	{
		obj := NewTypeName(nopos, nil, "comparable", nil)
		obj.setColor(black)

#include "tensorflow/core/lib/strings/strcat.h"

namespace tensorflow {

/// @defgroup core Core Tensorflow API

class Output;

/// @addtogroup core
/// @{

/// Represents a node in the computation graph.
class Operation {
 public:
  Operation() : node_(nullptr) {}
  explicit Operation(Node* n);

  int32 num_inputs() const { return node_->num_inputs(); }

import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A step in a pipeline of an asynchronous computation. When the last step in the computation is
 * complete, some objects captured during the computation are closed.
 *
 * <p>A pipeline of {@code ClosingFuture}s is a tree of steps. Each step represents either an

		}
	}

	return nil
}

// pkeEncrypt encrypt a plaintext message.
//
// It implements K-PKE.Encrypt according to FIPS 203 (DRAFT), Algorithm 13,
// although the computation of t and AT is done in parseEK.
func pkeEncrypt(cc *[CiphertextSize]byte, ex *encryptionKey, m *[messageSize]byte, rnd []byte) []byte {
	var N byte
	r, e1 := make([]nttElement, k), make([]ringElement, k)
	for i := range r {

// 2) Any resource stores except the last one.
// TODO(ycao): Store-load forwarding implemented here is only correct when
// computation is purely sequential (no concurrency). Need to support concurrent
// computation as well.
void ForwardStoreToLoad(Block* block) {
  // resource_handle_to_last_store_op keeps track of the most recent (last)

  for (Region* region : regions_with_token) {
    // Initialize the token with the special argument token. This gets mapped to
    // input token in the parent op or a new token for the entry computation.
    auto token = StringAttr::get(ctx, tensorflow::kXlaTokenArgNodeName);
    for (Operation& op : region->getOps()) {
      // Only communication related ops that needs to have token should have the

  // outlined, as the constant operand is replaced by an argument.
  pm.addPass(mlir::createInlinerPass({}, NoCanonicalization));
  // Lift resource operations out of device computation. This step needs to be
  // done after inlining.
  pm.addPass(mlir::TFDevice::CreateResourceOpLiftingPass());
  // Outline clusters into cluster functions.
  pm.addPass(mlir::TFDevice::CreateClusterOutliningPass());

		if cfg.ExperimentErr != nil {
			base.Fatal(cfg.ExperimentErr)
		}
	}

	// Set environment (GOOS, GOARCH, etc) explicitly.
	// In theory all the commands we invoke should have
	// the same default computation of these as we do,
	// but in practice there might be skew
	// This makes sure we all agree.
	cfg.OrigEnv = toolchain.FilterEnv(os.Environ())
	cfg.CmdEnv = envcmd.MkEnv()
	for _, env := range cfg.CmdEnv {

// AssignVariableOps.
std::unique_ptr<OperationPass<ModuleOp>> CreateUnfreezeConstantsPass();

// Creates a pass that duplicates constants that affect the shape of a tensor
// after some computation.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateDuplicateShapeDeterminingConstantsPass();

// Creates a pass that creates a RestoreV2 op in the initializer function with

  se::Stream* stream =
      ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr;
  Allocator* allocator = ctx->device()->GetAllocator({});

  // Computation output should always be a tuple.
  VLOG(2) << "Result tuple shape: " << output.on_host_shape().DebugString();
  VLOG(2) << "Result tuple shape (on device): "
          << output.on_device_shape().DebugString();