# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push"
else

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

if [[ $TFCI_PYCPP_SWAP_TO_BUILD_ENABLE == 1 ]]; then
   tfrun bazel build $TFCI_BAZEL_COMMON_ARGS --profile "$TFCI_OUTPUT_DIR/profile.json.gz" --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_pycpp_test"
else

for VERSION in "${SUPPORTED_VERSIONS[@]}"
do
  cp ../../../requirements_lock_"$VERSION".txt "requirements_lock_"$VERSION".txt"
  bazel run \
    --experimental_convenience_symlinks=ignore \
    --enable_bzlmod=false \
    //:requirements_"$VERSION"_release.update
  sed -i '/^#/d' requirements_lock_"$VERSION".txt
  mv "requirements_lock_"$VERSION".txt" ../../../requirements_lock_"$VERSION".txt
done

# This flag forcibly enables experimental MLIR bridge support.
config_setting(
    name = "enable_mlir_bridge",
    define_values = {"enable_mlir_bridge": "true"},
    visibility = ["//visibility:public"],
)

# This flag forcibly disables experimental MLIR bridge support.
config_setting(
    name = "disable_mlir_bridge",
    define_values = {"enable_mlir_bridge": "false"},
    visibility = ["//visibility:public"],
)

  }

  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 {

  TF_DeleteTensor(scalarTensor);
  TF_DeleteTensor(tensor_1X1X6);
  TF_DeleteTensor(tensor_1X6);
}

TEST(CAPI_EXPERIMENTAL, LibraryPluggableDeviceLoadFunctions) {
  // TODO(penpornk): Enable this test on Windows.
#if !defined(PLATFORM_WINDOWS)
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
  // Load the library.
  TF_Status* status = TF_NewStatus();
  string lib_path =

    Windows x64 & x86.

    *   **Windows x64 & x86 packages:**
        *   oneDNN optimizations are *enabled by default* on X86 CPUs

      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }

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 =