// host-device communication.
// CHECK: tf._TPUCompileMlir
// CHECK-SAME: tf.Rank
// CHECK-NOT: tf._XlaHostComputeMlir
// CHECK-NOT: tf._XlaRecvAtHost
// CHECK-NOT: tf._XlaSendFromHost

      location, concat_dim_type, concat_dimension_attr);

  // Correctly set output shapes of concat op output if output shape is
  // statically known. Since the shape of TPUExecute op must be the same
  // across logical devices, we refer to the shape of 0th logical device
  // computation output.
  mlir::Type output_type;
  auto input_type = mlir::cast<mlir::TensorType>(inputs[0].getType());

  if (input_type.hasRank()) {

func.func @do_not_hoist_ops_with_virtual_device(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) {
  %0:8 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*xf32>) {devices = {TPU_REPLICATED_CORE_0 = ["/device:TPU:0", "/device:TPU:1"]}, n = 2: i32} {
    %1 = "tf.Shape"(%ri) {device = "", T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>

import okhttp3.internal.readFieldOrNull

/**
 * Base Android reflection based SocketAdapter for the built in Android SSLSocket.
 *
 * It's assumed to always be present with known class names on Android devices, so we build
 * optimistically via [buildIfSupported].  But it also doesn't assume a compile time API.
 */
class StandardAndroidSocketAdapter(
  sslSocketClass: Class<in SSLSocket>,

...
BUILD SUCCESSFUL in 1m 30s
63 actionable tasks: 61 executed, 2 up-to-date

```

    rollout_config.enabled_for_xla_launch_ = true;
    rollout_config.enabled_for_compile_on_demand_ = true;
    rollout_config.enabled_for_gpu_ = true;

    // Set flag to enable using XLA devices. PJRT currently is only supported
    // for XLA devices.
    GetXlaDeviceFlags()->tf_xla_enable_xla_devices = true;

    // Add and setup the GPU device.
    auto device_type = DeviceType(DEVICE_GPU);

  // in an initialized context.
  for (auto d = devices.begin(); d != devices.end();) {
    if (absl::StrContains(d->get()->name(), "CPU:0")) {
      d = devices.erase(d);
    } else {
      ++d;
    }
  }

  status->status = tensorflow::unwrap(ctx)->AddDevices(std::move(devices));
}

void TFE_InsertConfigKeyValue(TFE_Context* ctx, const char* key,

}

// ContainerInfo contains information required by the runtime to consume prepared resources.
type ContainerInfo struct {
	// The Annotations for the container
	Annotations []kubecontainer.Annotation
	// CDI Devices for the container
	CDIDevices []kubecontainer.CDIDevice

// verification is empty or invalid.
var errBitrotHashAlgoInvalid = StorageErr("bit-rot hash algorithm is invalid")

// errCrossDeviceLink - rename across devices not allowed.
var errCrossDeviceLink = StorageErr("Rename across devices not allowed, please fix your backend configuration")

// errLessData - returned when less data available than what was requested.

  llvm::SmallVector<Value, 4> inputs;
  // The result values of the function.
  llvm::SmallVector<Value, 4> results;
  // The devices of the input values. It should have the same size as inputs.
  llvm::SmallVector<std::string, 4> input_devices;
  // The devices of the result values. It should have the same size as results.
  llvm::SmallVector<std::string, 4> result_devices;