}
    };
    ;

    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

      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

        // Given
        SmbSessionImpl session = new SmbSessionImpl(mockContext, "testhost", "testdomain", mockTransport);

        // Add some trees
        SmbTreeImpl tree1 = session.getSmbTree("share1", null);
        SmbTreeImpl tree2 = session.getSmbTree("share2", null);
        assertNotNull(tree1);
        assertNotNull(tree2);

        // When - Release session to trigger cleanup
        session.release();

  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.

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Verify the `username` and data shape { #verify-the-username-and-data-shape }

We verify that we get a `username`, and extract the scopes.

            EndpointPair.ordered(1, 1),
            EndpointPair.ordered(1, 3),
            EndpointPair.ordered(1, 2))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateStarShapedGraph() {
    putEdge(2, 1);

        assertEquals(testServer, msrpcShareGetInfo.servername);
        assertEquals(testSharename, msrpcShareGetInfo.sharename);
    }

    @ParameterizedTest
    @NullAndEmptySource
    @ValueSource(strings = { "", "share1", "\\\\server\\share", "C$", "ADMIN$", "IPC$" })
    void testConstructorWithDifferentSharenames(String sharename) {
        // Test with various sharename values