value {
              list_value {
              }
            }
          }
          values {
            key: "kwonlydefaults"
            value {
              none_value {
              }
            }
          }
          values {
            key: "annotations"
            value {
              dict_value {
              }
            }
          }

package main

func Float64D3(list [][][]float64, value float64) int {
	valueCount := 0
	for _, listValue := range list {
		valueCount += Float64D2(listValue, value)
	}
	return valueCount
}

func Float64(list []float64, value float64) int {
	valueCount := 0
	for _, listValue := range list {
		if listValue == value {
			valueCount++
		}
	}
	return valueCount
}

func.func @main() {
^bb0:
  // CHECK:      key: "listvalue"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   list {
  // CHECK-NEXT:     s: " \n"
  // CHECK-NEXT:   }
  // CHECK:      key: "value"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   s: " 0\n\000\000"
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_INT32", value = "\200\n\00\00", listvalue = ["\20\0A"]} : () -> tensor<2xi32>

namespace tensorflow {
namespace testing {
namespace {

constexpr absl::string_view kZeroArgInputSignatureTextProto = R"(
tuple_value: {
    values: {
      tuple_value: {
      }
    }
    values: {
      dict_value: {
      }
    }
}
)";

constexpr absl::string_view kSingleArgInputSignatureTextProto = R"(
tuple_value: {
    values: {
    tuple_value: {
        values: {
        tensor_spec_value: {

              "args": {
                "grpc.http2.max_pings_without_data": {
                  "int_value": 0
                },
                "grpc.keepalive_time_ms": {
                  "int_value": 10000
                },
                "grpc.keepalive_timeout_ms": {
                  "int_value": 20000
                }
              }
            }
          },
          "initial_metadata": [

    return failure();

  return success();
}

// Pattern matches the following reduction function for ArgMax/ArgMin:
// %0 = compare{GT}(%lhs_value, %rhs_value)
// %1 = compare{NE}(%lhs_value, %lhs_value)
// %2 = or(%0, %1)
// %3 = select(%2, %lhs_value, %rhs_value)
// %4 = compare{EQ}(%lhs_value, %rhs_value)
// %5 = compare{LT}(%lhs_index, %rhs_index)
// %6 = and(%4, %5)
// %7 = or(%2, %6)
// %8 = select(%7, %lhs_index, %rhs_index)

    return accuracy, loss_value

  iterator = iter(ds_train)
  accuracy = 0.0
  for step in range(flags.FLAGS.train_steps):
    accuracy, loss_value = distributed_train_step(next(iterator))
    if step % display_step == 0:
      tf.print('Step %d:' % step)
      tf.print('    Loss = %f' % loss_value)
      tf.print('    Batch accuracy = %f' % accuracy)

	)

	want = []monitoring.MetricDefinition{
		{
			Name:        "mixer_config_adapter_info_configs_total",
			Type:        "LastValue",
			Description: "The number of known adapters in the current config.",
		},
		{
			Name:        "mixer_config_attributes_total",
			Type:        "LastValue",
			Description: "The number of known attributes in the current config.",
		},
		{
			Name:        "mixer_config_handler_configs_total",

	tv_sec  int64
	tv_usec int64
}

func (tv *timeval) set_usec(x int32) {
	tv.tv_usec = int64(x)
}

type itimerspec struct {
	it_interval timespec
	it_value    timespec
}

type itimerval struct {
	it_interval timeval
	it_value    timeval
}

type sigeventFields struct {
	value  uintptr
	signo  int32
	notify int32
	// below here is a union; sigev_notify_thread_id is the only field we use

// Modify sends a modify event.
func (f *FakeWatcher) Modify(obj runtime.Object) {
	f.result <- Event{Modified, obj}
}

// Delete sends a delete event.
func (f *FakeWatcher) Delete(lastValue runtime.Object) {
	f.result <- Event{Deleted, lastValue}
}

// Error sends an Error event.
func (f *FakeWatcher) Error(errValue runtime.Object) {
	f.result <- Event{Error, errValue}
}