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) {

    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.

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

#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);

  return new GraphContext(name);
}

// Register the tracing implemented in this file as the default tracing engine.
static bool register_tracing = [] {
  RegisterTracingEngineFactory("graphdef", GraphTracingFactory);
  SetDefaultTracingEngine("graphdef").IgnoreError();
  return true;
}();

}  // namespace graph
}  // namespace tracing

		respRecorder := xhttp.NewResponseRecorder(w)

		// Setup a http request body recorder
		reqRecorder := &xhttp.RequestRecorder{Reader: r.Body}
		r.Body = reqRecorder

		// Create tracing data structure and associate it to the request context
		tc := mcontext.TraceCtxt{
			AmzReqID:         w.Header().Get(xhttp.AmzRequestID),
			RequestRecorder:  reqRecorder,
			ResponseRecorder: respRecorder,
		}

                       AbstractTensorHandle** result) {
  AbstractOperationPtr op(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(op->Reset("ZerosLike", /*raw_device_name=*/nullptr));
  if (isa<tracing::TracingOperation>(op.get())) {
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingOperation>(op.get())->SetOpName(
        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;

fi

# Note: the profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling