}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

    attributes {tf_quant.quantized_ops = ${quantized_ops}} {
      // Given the convolution takes 2 qint8 inputs and output a qint32.
      // The accumulation scale is (input_scale * filter_scale).
      // The accumulation zero point is 0.
      %accum_scale = "tf.Mul"(%input_scale, %filter_scale) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

}

// represents a scaling request for a resource.
message Scale {
  // Standard object metadata; More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ObjectMeta metadata = 1;

      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.

	{"kern.cp_time2", []_C_int{1, 71}},
	{"kern.cpustats", []_C_int{1, 85}},
	{"kern.domainname", []_C_int{1, 22}},
	{"kern.file", []_C_int{1, 73}},
	{"kern.forkstat", []_C_int{1, 42}},
	{"kern.fscale", []_C_int{1, 46}},
	{"kern.fsync", []_C_int{1, 33}},
	{"kern.global_ptrace", []_C_int{1, 81}},
	{"kern.hostid", []_C_int{1, 11}},
	{"kern.hostname", []_C_int{1, 10}},
	{"kern.intrcnt.nintrcnt", []_C_int{1, 63, 1}},

	{"kern.cp_time2", []_C_int{1, 71}},
	{"kern.cpustats", []_C_int{1, 85}},
	{"kern.domainname", []_C_int{1, 22}},
	{"kern.file", []_C_int{1, 73}},
	{"kern.forkstat", []_C_int{1, 42}},
	{"kern.fscale", []_C_int{1, 46}},
	{"kern.fsync", []_C_int{1, 33}},
	{"kern.global_ptrace", []_C_int{1, 81}},
	{"kern.hostid", []_C_int{1, 11}},
	{"kern.hostname", []_C_int{1, 10}},
	{"kern.intrcnt.nintrcnt", []_C_int{1, 63, 1}},

public final class GcFinalization {
  private GcFinalization() {}

  /**
   * 10 seconds ought to be long enough for any object to be GC'ed and finalized. Unless we have a
   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but

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");