ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    TFE_DeleteContextOptions(opts);
    TFE_Executor* executor = TFE_NewExecutor(
        /*is_async=*/false, /*enable_streaming_enqueue=*/true,
        /*in_flight_nodes_limit=*/0);
    TFE_ContextSetExecutorForThread(ctx, executor);

    TFE_DeleteContext(ctx);
    TFE_DeleteExecutor(executor);
  }

  {

                                                      TF_Status* status);

// Configure device placement policy logging for the eager executor. Note this
// policy is applied to any subsequent op executions.
TF_CAPI_EXPORT void TFE_ContextSetLogDevicePlacement(TFE_Context* ctx,
                                                     unsigned char enable,

  fi
}

# The Python package is not allowed to depend on any CUDA packages.
@test "Pip package doesn't depend on CUDA" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:build_pip_package, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\

start by trying one of the smaller/easier issues here i.e.
[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
and then take a look at the
[issues with the "contributions welcome" label](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
These are issues that we believe are particularly well suited for outside

void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                       TF_Status* status) {
  tensorflow::unwrap(ctx)->SetAllowSoftPlacement(enable);
}

void TFE_ContextSetLogDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                      TF_Status* status) {
  tensorflow::unwrap(ctx)->SetLogDevicePlacement(enable);
}

  TF_SetConfig(&options->session_options, proto, proto_len, status);
}

void TFE_ContextOptionsSetAsync(TFE_ContextOptions* options,
                                unsigned char enable) {
  options->async = enable;
}

void TFE_ContextOptionsSetDevicePlacementPolicy(
    TFE_ContextOptions* options, TFE_ContextDevicePlacementPolicy policy) {
  options->device_placement_policy = policy;
}

  virtual void SetAllowSoftPlacement(bool enable) = 0;

  // Configure device placement policy logging.
  virtual void SetLogDevicePlacement(bool enable) = 0;

  // Enables running eager ops as functions.
  virtual void SetRunEagerOpAsFunction(bool enable) = 0;

  // Enables rewriting jit_compile functions.
  virtual void SetJitCompileRewrite(bool enable) = 0;

  }

  Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  Status AddInputList(absl::Span<AbstractTensorHandle* const> inputs) override {

typedef struct GCSFile {
  const std::string path;
  const bool is_cache_enable;
  const uint64_t buffer_size;
  ReadFn read_fn;
  absl::Mutex buffer_mutex;
  uint64_t buffer_start ABSL_GUARDED_BY(buffer_mutex);
  bool buffer_end_is_past_eof ABSL_GUARDED_BY(buffer_mutex);
  std::string buffer ABSL_GUARDED_BY(buffer_mutex);

  GCSFile(std::string path, bool is_cache_enable, uint64_t buffer_size,
          ReadFn read_fn)
      : path(path),

      config->mutable_graph_options()->mutable_rewrite_options();
  rewrite_options->set_scoped_allocator_optimization(
      tensorflow::RewriterConfig::ON);
  rewrite_options->mutable_scoped_allocator_opts()->add_enable_op(
      "CollectiveReduce");

  if ((tensorflow::IsGoogleCudaEnabled() || tensorflow::IsBuiltWithROCm()) &&
      num_virtual_gpus > 0) {
    tensorflow::GPUOptions* gpu_options =