// configuring the persistor used in the DeviceCompiler. Please note that
// non-XLA devices aren't supported yet. This is because:
// 1. PjRtClient doesn't support data transfer for non-XLA devices yet
// 2. Fetching the PjRtClient for non-XLA devices is also not supported yet
Status GetOrCreatePjRtDeviceCompilerAndProfiler(
    const OpKernelContext& ctx, const XlaPlatformInfo& platform_info,

}

Device* DeviceSetup::GetDevice(const string& device_name) {
  if (device_mgr_ == nullptr) {
    return nullptr;
  }

  string full_device_name = absl::StrCat(
      "/job:localhost/replica:0/task:0/device:", device_name, ":0");
  Device* device;
  TF_CHECK_OK(device_mgr_->LookupDevice(full_device_name, &device));
  return device;
}

    VLOG(3) << ndef.DebugString();
    if (!ndef.device().empty()) {
      TF_ASSIGN_OR_RETURN(jit::DeviceId device_id,
                          device_info_cache->GetIdFor(ndef.device()));
      device_set.Insert(device_id);
    }
  }

  if (!n->assigned_device_name().empty()) {
    // TODO(sanjoy): We need this because EncapsulateSubgraphsPass drops device

    return signalPassFailure();
  }

  // Get the number of devices per host.
  int device_num = 0;
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(
          getOperation()->getParentOfType<ModuleOp>(), &devices)))
    return signalPassFailure();
  for (const auto& device_name : devices.device_names()) {
    if (device_name.has_type && device_name.type == "TPU") ++device_num;
  }

  // This is needed to register the XLA_GPU device.
  std::vector<std::unique_ptr<Device>> devices;
  TF_ASSERT_OK(DeviceFactory::AddDevices(
      SessionOptions(), "/job:localhost/replica:0/task:0", &devices));

  // Scope::ToGraph loses the assigned device name since it goes through
  // GraphDef/NodeDef which does not have a field for the assigned device name.
  Node* n = FindNodeByName(*graph, "shape");

    value_to_device.insert({result, device});
  }
}

// Checks if an operation can have TPU devices propagated through.
bool IsSupportedOpToSetDevice(Operation& op) {
  return IsSupportedExecutorOp(op) ||
         isa<TF::IdentityOp, TF::IdentityNOp, TF::ShapeOp>(op);
}

// Finds nonconflicting TPU device for an operation from its operands. If an
// operand has no device or a non TPU device, or if there are conflicting

					VolumeSource: v1.VolumeSource{
						GCEPersistentDisk: &v1.GCEPersistentDiskVolumeSource{
							PDName: "fake-device1",
						},
					},
				},
			},
		},
	}
	volumeSpec := &volume.Spec{Volume: &pod.Spec.Volumes[0]}
	devicePath := "fake/device/path"
	deviceMountPath := "fake/device/mount/path"
	generatedVolumeName, err := util.GetUniqueVolumeNameFromSpec(plugin, volumeSpec)
	if err != nil {

// product of possible devices and inference datatypes. For example, we might
// raise a chunk of sequential operations from a block all having attributes
// `{ 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

  tf_device.replicate() {n = 2 : i32, devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}} {
    tf_device.return
  }
  func.return

// CHECK:      tf_device.replicate
// CHECK-SAME: devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}
// CHECK-SAME: n = 2
// CHECK-NEXT:   tf_device.return

                                              Device* device,
                                              Tensor* output_tensor,
                                              StatusCallback done) const {
  done(errors::Unimplemented("XLA->XLA same-device copies not implemented."));
}

void XlaDeviceContext::CopyCPUTensorToDevice(const Tensor* cpu_tensor,
                                             Device* device,