Set  int    `json:"setId"`
}

func (op auditObjectOp) String() string {
	// Flatten the auditObjectOp
	return fmt.Sprintf("name=%s,pool=%d,set=%d", op.Name, op.Pool, op.Set)
}

// Add erasure set information to the current context
func auditObjectErasureSet(ctx context.Context, api, object string, set *erasureObjects) {
	if len(logger.AuditTargets()) == 0 {
		return
	}

	op := auditObjectOp{

   * assume that using setCount() to increase the count is permitted iff add()
   * is permitted and similarly for decrease/remove(). We assume that a
   * setCount() no-op is permitted if either add() or remove() is permitted,
   * though we also allow it to "succeed" if neither is permitted.
   */

  private void assertSetCount(E element, int count) {

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name = "mul");

// If `op_device` is non-empty, set the created op on that device.
TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                            const string& op_device, TF_Status* s,
                            const char* name = "min");

# ops. Since other languages use the same libtensorflow_framework.so, op
# libraries are language agnostic.
#
# This shared object is not used unless framework_shared_object=true (set in the
# configure script unconditionally); otherwise if it is false or undefined, the
# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that

		writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)
		return
	}
	vars := mux.Vars(r)
	op := madmin.SiteResyncOp(vars["operation"])
	var (
		status madmin.SRResyncOpStatus
		err    error
	)
	switch op {
	case madmin.SiteResyncStart:
		status, err = globalSiteReplicationSys.startResync(ctx, objectAPI, peerSite)
	case madmin.SiteResyncCancel:

  }

  @Test
  public void removeEdge_antiparallelEdges() {
    assume().that(graphIsMutable()).isTrue();

    putEdge(N1, N2);
    putEdge(N2, N1); // no-op

    assertThat(graphAsMutableGraph.removeEdge(N1, N2)).isTrue();
    assertThat(graph.adjacentNodes(N1)).isEmpty();
    assertThat(graph.edges()).isEmpty();

         * It would perhaps be better to do this more selectively instead of applying this transform so broadly and having
         * it just no-op in most cases.
         */
        registerTransform(Minify::class) {
            from.attribute(minified, false).attribute(artifactType, "jar")
            to.attribute(minified, true).attribute(artifactType, "jar")

                            /*use_function=*/!std::get<2>(GetParam()));
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_EQ(
      "Provided null gradient_function for 'Neg'.\nIf the intent is to treat "
      "this op as non-differentiable consider using RegisterNotDifferentiable "
      "or NotDifferentiableGradientFunction.",
      s.message());
  ASSERT_EQ(nullptr, outputs[0]);
}

      ImmutableDoubleArray.Builder builder = ImmutableDoubleArray.builder(RANDOM.nextInt(20));
      AtomicInteger counter = new AtomicInteger(0);
      while (counter.get() < 1000) {
        BuilderOp op = BuilderOp.randomOp();
        op.doIt(builder, counter);
      }
      ImmutableDoubleArray iia = builder.build();
      for (int j = 0; j < iia.length(); j++) {
        assertThat(iia.get(j)).isEqualTo((double) j);
      }
    }

  // device. If is_async=false (constructor argument) this means the ops have
  // run and have results. If is_async=true it means that all of the
  // device-specific executors have scheduled the op.
  //
  // Accepts inferred shapes for outputs (`expected_output_shapes`), which if
  // fully defined will avoid querying the shapes of the underlying
  // TensorHandles when ParallelTensor::Shape is called. This allows async