class ImmediateExecutionOperation : public AbstractOperation {
 public:
  virtual void Clear() = 0;

  // Returns the inputs of this op.
  virtual absl::Span<ImmediateExecutionTensorHandle* const> GetInputs()
      const = 0;
  virtual absl::Status SetInput(size_t index,
                                ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  public void testExtensiveWithAbsolutePrefix() throws IOException {
    // Inputs are /b/c/<every possible 10-character string of characters "a./">
    // Expected outputs are from realpath -s.
    doExtensiveTest("testdata/simplifypathwithabsoluteprefixtests.txt");
  }

  public void testExtensiveNoPrefix() throws IOException {
    /*
     * Inputs are <every possible 10-character string of characters "a./">
     *

  public void testExtensiveWithAbsolutePrefix() throws IOException {
    // Inputs are /b/c/<every possible 10-character string of characters "a./">
    // Expected outputs are from realpath -s.
    doExtensiveTest("testdata/simplifypathwithabsoluteprefixtests.txt");
  }

  public void testExtensiveNoPrefix() throws IOException {
    /*
     * Inputs are <every possible 10-character string of characters "a./">
     *

    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Combines multiple iterators into a single iterator. The returned iterator iterates across the
   * elements of each iterator in {@code inputs}. The input iterators are not polled until
   * necessary.
   *

  // watched inputs.
  void Watch(const AbstractTensorHandle*);
  // Records an operation with given inputs and outputs
  // on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,

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

			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",

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

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

## Summary

                "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
            {