input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }
  GradientTape::RecordOperation(
      op_name, tape_tensors, input_ids, input_dtypes,
      [gradient_function]() -> GradientFunction* { return gradient_function; },

//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

//go:build !race
// +build !race

// Tests in this file are not run under the `-race` flag as they are too slow
// and cause context deadline errors.

package cmd

import (
	"context"
	"fmt"
	"runtime"
	"testing"
	"time"

                    </mxGeometry>
                </mxCell>

    val result = dns.lookup("google.com")
    assertThat(result.size).isEqualTo(2)
    assertThat(result).contains(address("157.240.1.18"))
    assertThat(result).contains(address("2a03:2880:f029:11:face:b00c:0:2"))
    val request1 = server.takeRequest()
    assertThat(request1.method).isEqualTo("GET")
    val request2 = server.takeRequest()
    assertThat(request2.method).isEqualTo("GET")

                           ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
Status Execute(AbstractOperation*, AbstractContext*,
               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation*, Tape*, const GradientRegistry&);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_operation",
        ":c_api_unified_internal",
        "//tensorflow/c/experimental/gradients/tape:tape_operation",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
    ],
)

cc_library(
    name = "gradients_internal",
    srcs = [
        "gradients.cc",

==============================================================================*/
#include "tensorflow/c/eager/tracing_utils.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace tracing {

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

                        <Array as="points"/>
                    </mxGeometry>
                </mxCell>

	}
	bs.mu.RUnlock()

	return traceInfo
}

func (bs *bootstrapTracer) Publish(ctx context.Context, trace *pubsub.PubSub[madmin.TraceInfo, madmin.TraceType]) {
	for _, bsEvent := range bs.Events() {
		if bsEvent.Message != "" {
			select {
			case <-ctx.Done():
			default:
				trace.Publish(bsEvent)
			}
		}
	}