bool* executed_flag, TFE_CustomDevice** device,
                           void** device_info);
TFE_TensorHandle* UnpackTensorHandle(TFE_TensorHandle* logged_tensor_handle,
                                     TF_Status* status);

  env->SetNowSeconds(3);
  int value = 0;
  EXPECT_TRUE(cache.Lookup(key, &value));
  EXPECT_EQ(value, 42);
  // 2 seconds after the most recent insertion, the entry is no longer valid.
  env->SetNowSeconds(4);
  EXPECT_FALSE(cache.Lookup(key, &value));
  // Re-insert the entry.
  cache.Insert(key, 43);
  EXPECT_TRUE(cache.Lookup(key, &value));
  EXPECT_EQ(value, 43);

  status = env_->NewRandomAccessFile(filepath, &read_file);
  if (!status.ok())
    GTEST_SKIP() << "NewRandomAccessFile() not supported: " << status;

  char scratch[64 /* must be bigger than test_data */] = {0};
  StringPiece result;
  // read at least 1 byte more than test_data
  status = read_file->Read(0, test_data.size() + 1, &result, scratch);

    define_values = {"__TENSORFLOW_NO_LGPL_DEPS__": "1"},
    visibility = ["//visibility:public"],
)

# Config setting that disables the default logger, only logging
# to registered TFLogSinks
config_setting(
    name = "no_default_logger",
    define_values = {"no_default_logger": "true"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "force_rtti",
    define_values = {

                                                  TF_Function* function,
                                                  TF_Status* status);

// Removes a function from the context. Once removed, you can no longer
// TFE_Execute it or TFE_Execute any TFE_Op which has it as an attribute or any
// other function which calls it as an attribute.
TF_CAPI_EXPORT extern void TFE_ContextRemoveFunction(TFE_Context* ctx,

#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you

// Creates a new TensorHandle from memory residing in the physical device
// device_name. Takes ownership of the memory, and will call deleter to release
// it after TF no longer needs it or in case of error.
//
// Custom devices must use TFE_NewCustomDeviceTensorHandle instead.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(

    for the required packages. Alternatively, use the said
    [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build)
    (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no
    longer supported for) development. Use TF SIG Build Dockerfiles in
    development to avoid installing the packages directly on your system (in
    which case remember to change the directory from `/root` to `/tensorflow`

        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.
    *   The global policy can no longer be set to a non-floating point policy in
        `tf.keras.mixed_precision.experimental.set_policy`
    *   In `Layer.call`, `AutoCastVariable`s will no longer be casted within

  TF_RETURN_IF_ERROR(ValidatePluginMemoryRoutines(info));

  // Validate and register all filesystems
  // Try to register as many filesystems as possible.
  // Free memory once we no longer need it
  Status status;
  for (int i = 0; i < info->num_schemes; i++) {
    status.Update(ValidateAndRegisterFilesystems(info, i));
    info->plugin_memory_free(info->ops[i].scheme);