"@local_xla//xla/service:computation_placer",
        "//tensorflow/core",
        "//tensorflow/core/common_runtime/eager:context",
        "//tensorflow/core/common_runtime/eager:tensor_handle",
        "//tensorflow/core/config:flag_defs",
        "//tensorflow/core/config:flags",
        "//tensorflow/core/data/service:dispatcher_client",
        "//tensorflow/core/data/service:grpc_util",

      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

# pretend the path is a directory.
# 1. Upload nightlies
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/nightly/"
TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"

# gs://tensorflow/nightly/2.16.0-dev20240105 (nightly), overwriting previous values.
if [[ "$TFCI_ARTIFACT_FINAL_GCS_ENABLE" == 1 ]]; then
  gcloud auth activate-service-account --key-file="$TFCI_ARTIFACT_FINAL_GCS_SA_PATH"

  # $TF_VER_FULL will resolve to e.g. "2.15.0-rc2". Since $TF_VER_FULL comes
  # from get_versions.sh, which must be run *after* update_version.py, FINAL_URI

name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version
  (`Settings` > `Apps` > `Google Play Services` > `App details`):

// A simple logging device to test custom device registration.
#include <memory>

#include "absl/strings/match.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/custom_device_testutil.h"
#include "tensorflow/c/tf_status.h"

  const char* first_device = "/job:worker/replica:0/task:1/device:CPU:0";
  const char* second_device = "/job:worker/replica:0/task:2/device:CPU:0";
  const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  std::array<const char*, 2> underlying_devices{first_device, second_device};
  RegisterParallelDevice(context.get(), device_name, underlying_devices,
                         status.get());

  // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
  // chosen for the operation by the device placement logic in the
  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  TFE_DEVICE_PLACEMENT_WARN = 1,

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  const DLTensor* dltensor_out = &dlm_out->dl_tensor;
  EXPECT_EQ(dltensor_out->device.device_type, dltensor_in->device.device_type);
  EXPECT_EQ(dltensor_out->device.device_id, dltensor_in->device.device_id);
  EXPECT_EQ(dltensor_out->ndim, dltensor_in->ndim);
  EXPECT_EQ(dltensor_out->dtype.code, dltensor_in->dtype.code);