python3 -m pip install "$TF_WHEEL"
}

@test "TensorFlow is importable" {
    source /tf/venv/bin/activate
    python3 -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
}

# Is this still useful?
@test "TensorFlow has Keras" {
    source /tf/venv/bin/activate
    python3 -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'

      }
    };
    ;

    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<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

      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.

  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.

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,