audio/x-mpeg                   mp2              # MP2 Sound File
chemical/x-pdb                 pdb              # PDB Chemistry Model File
chemical/x-xyz                 xyz              # XYZ Chemistry Model File
drawing/x-dwf                  dwf              # AutoCAD
image/gif                      gif              # GIF image file
image/ief                      ief              # Image Exchange

        extensionMap.put("odp", "pdf");
        extensionMap.put("otp", "pdf");
        extensionMap.put("sxi", "pdf");
        extensionMap.put("ppt", "pdf");
        extensionMap.put("pptx", "pdf");
        // Drawing Formats
        extensionMap.put("odg", "svg");
        extensionMap.put("otg", "svg");

        extractorMap.put("pdf", new PdfExtractor());
        extractorMap.put("svg", new XmlExtractor());
    }

# See the License for the specific language governing permissions and
# limitations under the License.

# Usage: tracing::extract_prow_trace.
# If running in a prow job, this sets the parent trace to the same value Prow tracing will use, as defined in https://github.com/kubernetes/test-infra/issues/30010
function tracing::extract_prow_trace() {
  if [[ "${PROW_JOB_ID:-}" != "" ]]; then
    local trace
    trace="$(<<< "$PROW_JOB_ID" tr -d '\-')"

// This allows the client to swap the implementation of the tracing engine.
// Any future call to TF_CreateFunction will use the implementation defined
// here.
void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as
// such it can trace operations through TF_ExecuteOperation. After completing
// tracing, a function can be obtained by TF_FinalizeFunction.

using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;
using tensorflow::tracing::SetDefaultTracingEngine;
using tensorflow::tracing::TracingContext;
using tensorflow::tracing::TracingOperation;
using tensorflow::tracing::TracingTensorHandle;

void TF_SetTracingImplementation(const char* name, TF_Status* s) {

struct OutputList {
  std::vector<AbstractTensorHandle*> outputs;
  int expected_num_outputs = -1;
};

namespace tracing {

// =============================================================================
// Implementation detail for the unified execution APIs for Eager and tracing
// backends (graph/MLIR).
//
// This defines a set of abstract classes that are intended to provide the

    sidecar.istio.io/inject: "false"
  ingress_enabled: false
  image_pull_policy: IfNotPresent
login_token:
  signing_key: CHANGEME00000000
external_services:
  # Kiali will not start up without tracing service. We don't want to require it.
  tracing:

- **Purpose**: Tests related to telemetry features, including metrics, logging, and tracing.
- **Focus**:
  1. Collection and processing of telemetry data.
  1. Interaction between telemetry components and Istio control plane.
  1. Validation of metrics collection and reporting.
  1. Testing of logging configurations and log collection.
  1. Validation of tracing and distributed tracing setups.

#include <utility>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}

#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:
  explicit GraphFunction(FunctionDef fdef);