for _, result := range results {
				totalPut += result.Uploads
				totalGet += result.Downloads
			}

			if totalGet < throughputHighestGet {
				// Following check is for situations
				// when Writes() scale higher than Reads()
				// - practically speaking this never happens
				// and should never happen - however it has
				// been seen recently due to hardware issues
				// causes Reads() to go slower than Writes().

			field.Size = -1
		}
	}

	if p, ok := field.TagSettings["PRECISION"]; ok {
		field.Precision, _ = strconv.Atoi(p)
	}

	if s, ok := field.TagSettings["SCALE"]; ok {
		field.Scale, _ = strconv.Atoi(s)
	}

	// default value is function or null or blank (primary keys)
	field.DefaultValue = strings.TrimSpace(field.DefaultValue)

      This transformation pass modifies the input and output types of the function
      to what are specified. The task was not just adding cast operations, but,
      instead, using tfl.quantize and tfl.dequantize ops to scale the tensors.
  }];
  let constructor = "CreateModifyIONodesPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
  let options = [
      ListOption<"io_node_types_", "test-io-types", "std::string",

		return endpoints
	}

	// A new array of endpoints to be returned that will have both local and
	// remote gateways (if any)
	filtered := make([]*LocalityEndpoints, 0)

	// Scale all weights by the lcm of gateways per network and gateways per cluster.
	// This will allow us to more easily spread traffic to the endpoint across multiple
	// network gateways, increasing reliability of the endpoint.

}

// Creates a new `tfl.qconst` op for the bias. The bias values are 0s, because
// this bias a dummy bias (note that bias fusion is not considered for this
// transformation). The quantization scale for the bias is input scale *
// filter scale. `filter_const_op` is used to retrieve the filter scales and
// the size of the bias constant.
TFL::QConstOp CreateTflConstOpForDummyBias(

	unsafePoint       bool      // this op is an unsafe point, i.e. not safe for async preemption
	symEffect         SymEffect // effect this op has on symbol in aux
	scale             uint8     // amd64/386 indexed load scale
}

type inputInfo struct {
	idx  int     // index in Args array
	regs regMask // allowed input registers
}

type outputInfo struct {
	idx  int     // index in output tuple

  // continuing. When scaling down, the pods are removed in the opposite order.
  // The alternative policy is `Parallel` which will create pods in parallel
  // to match the desired scale without waiting, and on scale down will delete
  // all pods at once.
  // +optional
  optional string podManagementPolicy = 6;

  // updateStrategy indicates the StatefulSetUpdateStrategy that will be

colors are first mapped into HSV. A scale is then applied all the saturation
values, and then remapped back to RGB colorspace.
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Float16, TF_Float32]>, [{Images to adjust.  At least 3-D.}]>:$images,
    Arg<TF_Float32Tensor, [{A float scale to add to the saturation.}]>:$scale
  );

  let results = (outs

{% block metas %}
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
{% endblock %}

<title>Simple Bookstore App</title>
{% block scripts %}
<script src="static/tailwind/tailwind.css"></script>
<script type="text/javascript">
  window.addEventListener("DOMContentLoaded", (event) => {
    const dialog = document.querySelector("dialog");

	// is likely to be the originator, so requeuing would hot-loop us.  Failures are requeued by the workqueue directly.
	// This is a low traffic and scale resource, so the copy is terrible.  It's not good, so better ideas
	// are welcome.
	oldCopy := oldCRD.DeepCopy()
	newCopy := newCRD.DeepCopy()
	oldCopy.ResourceVersion = ""
	newCopy.ResourceVersion = ""