}
    };
    ;

    public 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;

      }
    };
    ;

    public 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;

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

      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

 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.

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());

  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.

   * |      |
   * e ---- c ---- f
   * }</pre>
   */
  private static final SuccessorsFunction<Character> JAVADOC_GRAPH =
      createUndirectedGraph("ba", "ad", "be", "ac", "ec", "cf");

  /**
   * A diamond shaped directed graph (arrows going down):
   *
   * <pre>{@code
   *   a
   *  / \
   * b   c
   *  \ /
   *   d
   * }</pre>
   */
  private static final SuccessorsFunction<Character> DIAMOND_GRAPH =