unsigned char should_enable);

// Create a serialized tensorflow.ConfigProto proto, where:
//
// a) ConfigProto.optimizer_options.global_jit_level is set to ON_1 if
// `enable_xla_compilation` is non-zero, and OFF otherwise.
// b) ConfigProto.gpu_options.allow_growth is set to `gpu_memory_allow_growth`.
// c) ConfigProto.device_count is set to `num_cpu_devices`.
TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig(

# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a
# bazel-out directory. Next, it archives the generated source files
# into a srcjar directly under the root. There doesn't appear to be a

    if varname in environ_cp:
      write_to_bazelrc('build --define=%s=%s' % (varname, environ_cp[varname]))


def set_windows_build_flags(environ_cp):
  """Set Windows specific build options."""

  # First available in VS 16.4. Speeds up Windows compile times by a lot. See
  # https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
  # pylint: disable=line-too-long

// --------------------------------------------------------------------------
// TF_SessionOptions holds options that can be passed during session creation.
typedef struct TF_SessionOptions TF_SessionOptions;

// Return a new options object.
TF_CAPI_EXPORT extern TF_SessionOptions* TF_NewSessionOptions(void);

// Set the target in TF_SessionOptions.options.
// target can be empty, a single entry, or a comma separated list of entries.

      description: TensorFlow installed from
      options:
        - source
        - binary
    validations:
      required: true

  - type: input
    id: tfversion
    attributes:
      label: TensorFlow version
      placeholder: e.g., tf 2.8
    validations:
      required: true
  - type: dropdown
    id: Code
    attributes:
      label: Custom code
      options:
        - "Yes"
        - "No"

                                   PJRT_NamedValue* create_options,
                                   int num_options) {
  absl::StatusOr<std::unique_ptr<xla::PjRtClient>> pjrt_client =
      xla::GetCApiClient(device_type, pjrt::ConvertFromPjRtNamedValueList(
                                          create_options, num_options));
  if (!pjrt_client.ok()) {
    status->status = pjrt_client.status();
    return;

TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void);

// Populates a TF_ThreadOptions struct with system-default values.
TF_CAPI_EXPORT extern void TF_DefaultThreadOptions(TF_ThreadOptions* options);

// Returns a new thread that is running work_func and is identified
// (for debugging/performance-analysis) by thread_name.
//
// The given param (which may be null) is passed to work_func when the thread

# set in the shell environment, those variables will be restored after the
# TFCI env has been loaded. This is useful for e.g. on-demand "generic" jobs
# where the user may wish to change just one option.
if [[ -z "${TFCI:-}" ]]; then
  echo '==TFCI==: The $TFCI variable is not set. This is fine as long as you'
  echo 'already sourced a TFCI env file with "set -a; source <path>; set +a".'

class GraphErrorInjectionPass : public tensorflow::GraphOptimizationPass {
 public:
  static bool enabled_;
  GraphErrorInjectionPass() {}

  tensorflow::Status Run(
      const tensorflow::GraphOptimizationPassOptions& options) override {
    if (!enabled_) {
      return absl::OkStatus();
    }
    if (first_call_) {
      first_call_ = false;
      return absl::OkStatus();
    }

// integration testing rather than purely testing the parallel device. They
// correspond fairly well to the implementation, but testing the C++ directly is
// another option.

namespace tensorflow {
namespace parallel_device {

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,