public:
  // Add a function parameter and return the corresponding tensor.
  virtual absl::Status AddParameter(DataType dtype,
                                    const PartialTensorShape& shape,
                                    TracingTensorHandle**) = 0;

  // Finalize this context and make a function out of it. The context is in a
  // invalid state after this call and must be destroyed.

  TFE_TensorDebugInfo* debug_info = TFE_TensorHandleTensorDebugInfo(h, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  ASSERT_EQ(2, TFE_TensorDebugInfoOnDeviceNumDims(debug_info));
  // Shape is the same for CPU tensors.
  EXPECT_EQ(3, TFE_TensorDebugInfoOnDeviceDim(debug_info, 0));
  EXPECT_EQ(2, TFE_TensorDebugInfoOnDeviceDim(debug_info, 1));

  TFE_DeleteTensorDebugInfo(debug_info);
  TFE_DeleteTensorHandle(h);

        mockService.unregisterWitness(regId);
    }

    @Test
    void testMultipleRegistrations() {
        // Register multiple witnesses
        String reg1 = mockService.registerWitness("\\\\server1\\share1", "192.168.1.100", 1);
        String reg2 = mockService.registerWitness("\\\\server2\\share2", "192.168.1.101", 1);
        String reg3 = mockService.registerWitness("\\\\server3\\share3", "192.168.1.102", 1);

            when(mockReferral.getTtl()).thenReturn(300);
            when(mockReferral.getRFlags()).thenReturn(0);
            when(mockReferral.getNode()).thenReturn("\\server1\\share1\\path1");
            first = DfsReferralDataImpl.fromReferral(mockReferral, "\\\\server1\\share1\\path1", System.currentTimeMillis() + 10000, 20);

            // Setup second referral
            DfsReferralDataImpl second = new DfsReferralDataImpl();

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
                  const char* name = "neg");

TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);

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,

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_OpSetAttrType(op.get(), "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op.get(), "shape", {}, 0, status.get());
  TFE_OpSetAttrString(op.get(), "container", "", 0);
  TFE_OpSetAttrString(op.get(), "shared_name", "", 0);
  TFE_OpSetDevice(op.get(), name, status.get());

      }
    };
    ;

    abstract byte[] hash(Algorithm algorithm, byte[] input);
  }

  private enum Algorithm {
    MD5("MD5", Hashing.md5()),
    SHA_1("SHA-1", Hashing.sha1()),
    SHA_256("SHA-256", Hashing.sha256()),
    SHA_384("SHA-384", Hashing.sha384()),
    SHA_512("SHA-512", Hashing.sha512());

    private final String algorithmName;

      return samples;
    }

    @Override
    public Set<K> create(Object... elements) {
      @SuppressWarnings("unchecked")
      K[] keysArray = (K[]) elements;

      // Start with a suitably shaped collection of entries
      Collection<Entry<K, V>> originalEntries = mapGenerator.getSampleElements(elements.length);

      // Create a copy of that, with the desired value for each key

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

      return samples;
    }

    @Override
    public Set<V> create(Object... elements) {
      @SuppressWarnings("unchecked")
      V[] valuesArray = (V[]) elements;

      // Start with a suitably shaped collection of entries
      Collection<Entry<K, V>> originalEntries = mapGenerator.getSampleElements(elements.length);

      // Create a copy of that, with the desired value for each value