private final ImmutableMap<E, Integer> inputSet;
    private final int mask;

    SubSet(ImmutableMap<E, Integer> inputSet, int mask) {
      this.inputSet = inputSet;
      this.mask = mask;
    }

    @Override
    public Iterator<E> iterator() {
      return new UnmodifiableIterator<E>() {
        final ImmutableList<E> elements = inputSet.keySet().asList();
        int remainingSetBits = mask;

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

`Generators` get as input the `Proxy` (a representation of the current client), the current `PushContext` snapshot, and a list of config updates that caused the change.

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["body", "extra"],
            "msg": "Extra inputs are not permitted",
            "input": "Mr. Poopybutthole"
        }
    ]
}
```

## Summary

			description:       "Invalid command args - missing valid input spec",
			args:              strings.Split("entry configure --name foo -o temp --clusterID cid", " "),
			expectedException: true,
			expectedOutput:    "Error: expecting a WorkloadGroup artifact file or the name and namespace of an existing WorkloadGroup\n",
		},
		{
			description:       "Invalid command args - missing valid input spec",

                "detail": [
                    {
                        "type": "extra_forbidden",
                        "loc": ["cookie", "extra"],
                        "msg": "Extra inputs are not permitted",
                        "input": "track-me-here-too",
                    }
                ]
            }
        )
        | IsDict(
            # TODO: remove when deprecating Pydantic v1
            {

// capture some inputs and then add a node in the graph. The output tensors are
// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s);

// TPUReplicatedInput and TPUReplicatedOutput respectively. For example, with
// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =
// TPUReplicatedOutput(input=x, num_replicas=2)` un-packs the parallel tensor
// into its components.
//

                IsDict(
                    {
                        "type": "extra_forbidden",
                        "loc": ["header", "tool"],
                        "msg": "Extra inputs are not permitted",
                        "input": "plumbus",
                    }
                )
                | IsDict(
                    # TODO: remove when deprecating Pydantic v1
                    {

    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;
    if (dev->device_name == input_device) {
      LoggedTensor* t = reinterpret_cast<LoggedTensor*>(
          TFE_TensorHandleDevicePointer(input, s));
      if (TF_GetCode(s) != TF_OK) return;