*   Iterator<Long> it = set.iterator(); // Must be in synchronized block
   *   while (it.hasNext()) {
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned bimap will be serializable if the specified bimap is serializable.
   *
   * @param bimap the bimap to be wrapped in a synchronized view

   *   Iterator<Long> it = set.iterator(); // Must be in synchronized block
   *   while (it.hasNext()) {
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned bimap will be serializable if the specified bimap is serializable.
   *
   * @param bimap the bimap to be wrapped in a synchronized view

// RUN: tac-opt-all-backends -tfl-device-transform-nnapi %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @mean_4d_keepdim(%arg0: tensor<1x48x48x512xf32>) -> tensor<1x1x1x512xf32> {
  %cst = arith.constant dense<[1, 2]> : tensor<2xi32>
  %0 = "tfl.mean"(%arg0, %cst) {keep_dims = true} : (tensor<1x48x48x512xf32>, tensor<2xi32>) -> tensor<1x1x1x512xf32>
  func.return %0 : tensor<1x1x1x512xf32>
}

		if ld.AllowErrors {
			// The user is explicitly ignoring these errors, so don't bother them with
			// other options.
			return
		}

		// We print directly to os.Stderr because this information is advice about
		// how to fix errors, not actually an error itself.
		// (The actual errors should have been logged already.)

		fmt.Fprintln(os.Stderr)

		goFlag := ""

		runtime.ExitSyscall()
	}
	if r0 != 0 {
		err = errnoErr2(e1, e2)
	}
	return
}

// Dummy function: there are no semantics for Madvise on z/OS
func Madvise(b []byte, advice int) (err error) {
	return
}

func Mmap(fd int, offset int64, length int, prot int, flags int) (data []byte, err error) {
	return mapper.Mmap(fd, offset, length, prot, flags)
}

// `{ tac.device = "GPU", tac.inference_type = "FLOAT"}` to a function
// with the matching attributes. Assumed is that device type "CPU"
// is the only device that is allowed to call other devices. I.e. ancestors of a
// "CPU" `Operation` may only `Operations` without a device or other "CPU"
// `Operations`. Implied is that "CPU" ops may contain subgraphs of different

// Creates a pass that extracts outside compilation (Host ops inside device
// cluster) at head/tail of Device cluster to run before/after XLA computation.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
CreateExtractHeadTailOutsideCompilationPass();

// Creates a pass that extract outside compilation (Host ops inside cevice
// cluster) ops to a separate parallel_execute region to run on CPU.

    return std::nullopt;

  if (!HasValidHardwareTarget(op)) return std::nullopt;

  auto device = op->getAttrOfType<mlir::StringAttr>(mlir::TFL::tac::kDevice);
  if (device == nullptr) return std::nullopt;

  llvm::StringRef device_name_str = device.getValue();
  return device_name_str.str();
}

std::optional<std::vector<float>> GetPerDeviceCosts(

    // TODO(jpienaar): Remove this later.
    if (auto device = res->getAttrOfType<StringAttr>("device")) {
      if (!device.getValue().empty())
        result_id->setAttr("device", device);
      else
        result_id->setAttr("device", compile_device_op);
    } else if (compile_device_op) {
      result_id->setAttr("device", compile_device_op);
    }

                                       mlir::Builder* builder) {
  // Parse GPU device compute capability from physical device description.
  static auto* r = new llvm::Regex("compute capability: ([0-9]+)\\.([0-9]+)");

  llvm::SmallVector<llvm::StringRef, 3> cc;
  if (r->match(device.attributes().physical_device_desc(), &cc)) {
    return mlir::TF::GpuDeviceMetadata::get(