<label for="output" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_stemmeroverride_target"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="output"/>
                                        <la:text styleId="output" property="output" styleClass="form-control"/>

   * (key) bits 'affects' a strictly smaller set of output bits. Funneling is bad because it can
   * result in more-than-ideal collisions for a non-uniformly distributed key space. In practice,
   * most key spaces are ANYTHING BUT uniformly distributed. A bit(i) in the input is said to
   * 'affect' a bit(j) in the output if two inputs, identical but for bit(i), will differ at output
   * bit(j) about half the time
   *

    return stream.readInt();
  }

  /**
   * Stores the contents of a map in an output stream, as part of serialization. It does not support
   * concurrent maps whose content may change while the method is running.
   *
   * <p>The serialized output consists of the number of entries, first key, first value, second key,
   * second value, and so on.
   */

    validations:
      required: true

  - type: textarea
    id: expected-behavior
    attributes:
      label: "What did you expect to see?"

* mangle
-N ISTIO_PRERT
-N ISTIO_OUTPUT
-A PREROUTING -j ISTIO_PRERT
-A OUTPUT -j ISTIO_OUTPUT
-A ISTIO_PRERT -m mark --mark 0x539/0xfff -j CONNMARK --set-xmark 0x111/0xfff
-A ISTIO_PRERT -s 169.254.7.127 -p tcp -m tcp -j ACCEPT
-A ISTIO_PRERT ! -d 127.0.0.1/32 -p tcp -i lo -j ACCEPT
-A ISTIO_PRERT -p tcp -m tcp --dport 15008 -m mark ! --mark 0x539/0xfff -j TPROXY --on-port 15008 --tproxy-mark 0x111/0xfff

  }
  return attr;
}

TensorId ToTensorId(const TF_Output& output) {
  return TensorId(output.oper->node.name(), output.index);
}

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
std::vector<tensorflow::Output> OutputsFromTFOutputs(TF_Output* tf_outputs,
                                                     int n) {
  std::vector<tensorflow::Output> outputs(n);
  for (int i = 0; i < n; ++i) {
    outputs[i] =

        final String keyProp = NAME_PREFIX + id;
        requestBody.put(KEY_PROPERTY, keyProp);
        requestBody.put("output", "output");
        return requestBody;
    }

    @Override
    protected Map<String, Object> getUpdateMap() {
        final Map<String, Object> updateMap = new HashMap<>();
        updateMap.put("output", "new_output");
        return updateMap;
    }

    @Test
    void crudTest() {
        testCreate();

	util.CompareContent(t, gotOut.Bytes(), "testdata/ecds/output.txt")
}

func TestPrintEcdsYaml(t *testing.T) {
	gotOut := &bytes.Buffer{}
	cw := &ConfigWriter{Stdout: gotOut}
	cd, _ := os.ReadFile("testdata/ecds/configdump.json")
	cw.Prime(cd)
	err := cw.PrintEcds("yaml")
	assert.NoError(t, err)

	util.CompareContent(t, gotOut.Bytes(), "testdata/ecds/output.yaml")
}

func TestPrintEcdsJSON(t *testing.T) {

Example:

```sh
minio server /data{1...4}
```

The command takes no flags

```sh
mc support diagnostics myminio/
```

The output printed will be of the form

```sh
● Admin Info ... ✔ 
● CPU ... ✔ 
● Disk Hardware ... ✔ 
● Os Info ... ✔ 
● Mem Info ... ✔ 
● Process Info ... ✔ 
● Config ... ✔ 
● Drive ... ✔ 
● Net ... ✔ 

```

### Options

Opt | Description
--- | ---
-d \<n\> | emit values at depth n. if n < 0, all values will be emitted
-kv | output inner key value pairs as newly formed objects
-v | output depth and offset details for each value
-h | display help dialog

## Benchmarks