// reaching feature parity with the old quantizer for dynamic range
// quantization, specifically for
// third_party/tensorflow/lite/tools/optimize/quantize_weights.h.
// TODO(b/202468183): Selective quantization + quant debugger support for
// dynamic range quantization for verify_numeric and whole_model_verify flags.
TfLiteStatus QuantizeWeights(
    flatbuffers::FlatBufferBuilder* builder, const tflite::Model* input_model,

You may also want to remove the Jaeger deployment, which will not be used, with `kubectl delete deployment jaeger`, or avoid installing it
to begin with by following the selective installation steps in [Getting Started](#getting-started).

For more information about integrating with Zipkin, please see the [Zipkin integration page](https://istio.io/docs/tasks/observability/distributed-tracing/zipkin/).

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

				AuthInfos: map[string]*clientcmdapi.AuthInfo{
					"*":       {Token: "star"},
					"bar.com": {Token: "bar"},
				},
			},
			expected: rest.Config{BearerToken: "star"},
		},
		{
			name:       "most selective",
			serverName: "one.two.three.com",
			kubeconfig: clientcmdapi.Config{
				AuthInfos: map[string]*clientcmdapi.AuthInfo{
					"*.two.three.com": {Token: "first"},
					"*.three.com":     {Token: "second"},