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) {

// 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.

#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() {}

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

#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);

        createTest("SomeOtherTest5")
        then:
        def result = gradleRunner("test").buildAndFail()
        result.output.contains("total executions: 2") == false
        and: "no jvm system exit tracing provided"
        result.output.contains("""Test jvm exited unexpectedly.
Test jvm system exit trace:""") == false
    }

    def "all tests are rerun when test jvm has crashed"() {
        when:

#!/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 linux/riscv64"
}

  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;