*   <li>remove();
 *   <li>next();
 * </ol>
 *
 * <p>This particular order of operations may be unrealistic, and testing all 3^5 of them may be
 * thought of as overkill; however, it's difficult to determine which proper subset of this massive
 * set would be sufficient to expose any possible bug. Brute force is simpler.
 *
 * <p>To use this class the concrete subclass must implement the {@link

  //
  // TODO: Determine how to detect ContentType and ContentEncoding of 'Raw' data.
  optional bytes raw = 1;
}

// TypeMeta is shared by all top level objects. The proper way to use it is to inline it in your type,
// like this:
//
// 	type MyAwesomeAPIObject struct {
// 	     runtime.TypeMeta    `json:",inline"`
// 	     ... // other fields
// 	}
//

See [Istio CA Integration with SPIRE](https://istio.io/latest/docs/ops/integrations/spire) for further details about this integration.

## Deploy the integration

1. Deploy SPIRE. For proper socket injection, this **must** be done prior to installing Istio in your cluster:

  ```bash
  $ kubectl apply -f spire-quickstart.yaml
  ```

  if (!model_verifier.VerifyBuffer<Model>()) {
    std::cerr << "Verification failed.\n";
    return true;
  }
  return false;
}

// A FlatBuffer visitor that outputs a FlatBuffer as a string with proper
// indention for sequence fields.
// TODO(wvo): ToStringVisitor already has indentation functionality, use
// that directly instead of this sub-class?
struct IndentedToStringVisitor : flatbuffers::ToStringVisitor {

    // After we creat the alternative subgraph, we can still do canonicalization
    // legalization & other optimizations as long as we're not inlining the
    // function.
    // And in fact, we probably need to do the proper legalization, for the
    // compute cost to work. (in case we added some TF ops)
    pass_manager->addPass(mlir::TFL::CreatePrepareTFPass(
        /*unfold_batch_matmul=*/true,

  //
  // TODO: Determine how to detect ContentType and ContentEncoding of 'Raw' data.
  optional bytes raw = 1;
}

// TypeMeta is shared by all top level objects. The proper way to use it is to inline it in your type,
// like this:
//
// 	type MyAwesomeAPIObject struct {
// 	     runtime.TypeMeta    `json:",inline"`
// 	     ... // other fields
// 	}
//

// will be reused.
//
// This pass clones functions as needed to establish the invariant that all
// functions have a single use. This can in principle cause exponential code
// size bloat, and should in general be guided by a proper cost model.
//
// There are two factors which should be considered by a principled replacement
// to this pass:
//
// 1. TF currently relies on "sufficiently good shape inference" for

//     plugin or core implementation:
//     * Struct that should be filled by the plugin: `TP_OptimizerConfigs`,
//       `TP_Optimizer`, `TP_OptimizerRegistrationParams`
//     * Struct that should be filled by the proper: `TF_GrapplerItem`,
//       `TF_GraphProperties`, `TF_FunctionLibraryDefinition`
//   * We use `struct_size` for version checking. It should be set both by
//     core and the plugin.

		{
			name:      "missing cached value",
			actual:    []string{"foo", "bar", "foobar"},
			cached:    []string{"bar", "foo"},
			missing:   []string{"foobar"},
			redundant: []string{},
		},
		{
			name:      "proper cache set",
			actual:    []string{"foo", "bar", "foobar"},
			cached:    []string{"bar", "foobar", "foo"},
			missing:   []string{},
			redundant: []string{},
		},
	}

	for _, test := range tests {

	f, err := ioutil.TempFile("", "k8s_audit_webhook_test_")
	require.NoError(t, err, "creating temp file")

	defer func() {
		f.Close()
		os.Remove(f.Name())
	}()

	// NOTE(ericchiang): Do we need to use a proper serializer?
	require.NoError(t, stdjson.NewEncoder(f).Encode(config), "writing kubeconfig")

	retryBackoff := wait.Backoff{
		Duration: 500 * time.Millisecond,
		Factor:   1.5,
		Jitter:   0.2,
		Steps:    5,