*/
  HashCode hashBytes(byte[] input, int off, int len);

  /**
   * Shortcut for {@code newHasher().putBytes(input).hash()}. The implementation <i>might</i>
   * perform better than its longhand equivalent, but should not perform worse.
   *
   * @since 23.0
   */
  HashCode hashBytes(ByteBuffer input);

  /**
   * Shortcut for {@code newHasher().putUnencodedChars(input).hash()}. The implementation

  // 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,

  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.
   *

```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.
//