panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),
      panels.timeSeries.base('Goroutines', queries.goroutines, 'Goroutine count for each running instance'),
    ]),
  ], panelHeight=10, startY=1)
  + g.util.grid.makeGrid([
    row.new('Push Information')
    + row.withPanels([
      panels.timeSeries.xdsPushes(

              rate(
                go_memstats_mallocs_total{%(appLabels)s}
              [$__rate_interval])
            )
          |||
        ),
      ],

      goroutines:
        self.query(
          'Goroutines ({{pod}})',
          |||
            sum by (pod) (
              go_goroutines{%(appLabels)s}
            )
          |||
        ),

      connections:
        [

 *  </p>
 *  <p><code>WriteContext.write(object)</code> then delegates to {@link org.gradle.internal.serialize.graph.Codec#encode(WriteContext, java.lang.Object, kotlin.coroutines.Continuation) Codec.encode(WriteContext, Object)}.</p>
 *  <p>A codec implementation will, generally:
 *  <ul>

// and NumericVerify ops to compare output values from the quantized and float
// ops.
//
// When `legacy_float_scale` is true, the quantizer will use float scale instead
// of double, and call TOCO's quantization routines to maintain bit-exactness of
// the values with the TOCO quantizer.
TfLiteStatus QuantizeModel(
    absl::string_view model_buffer, const tflite::TensorType &input_type,
    const tflite::TensorType &output_type,