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);

	expectedOutput := msg.NewUpdateIncompatibility(ObjectToInstance(zipkinSvc),
		"meshConfig.defaultConfig.tracer", "1.21",
		"tracing is no longer by default enabled to send to 'zipkin.istio-system.svc'; "+
			"follow https://istio.io/latest/docs/tasks/observability/distributed-tracing/telemetry-api/",
		"1.21")

	assert.Equal(t, expectedOutput, messages[0])
}

func init() {

#!/bin/bash

set -e
# Enable tracing if set.
[ -n "$BASH_XTRACEFD" ] && set -x

function _init() {
	## All binaries are static make sure to disable CGO.
	export CGO_ENABLED=0

	## List of architectures and OS to test coss compilation.
	SUPPORTED_OSARCH="linux/ppc64le linux/mips64 linux/amd64 linux/arm64 linux/s390x darwin/arm64 darwin/amd64 freebsd/amd64 windows/amd64 linux/arm linux/386 netbsd/amd64 linux/mips openbsd/amd64"
}

  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,
		}