for dtype in dtype_list:
  lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
  exp = tf.constant([0, 0, 3, 10], dtype=tf.float32)

  res = bitwise_ops.bitwise_and(lhs, rhs)
  tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
```
  }];

  let arguments = (ins
    TF_IntTensor:$x,
    TF_IntTensor:$y
  );

  let results = (outs

*   Register devices under their legacy names in device_mgr to ease the
    transition to clusterspec-propagated configurations.
*   VectorExponential added to distributions.
*   Add a bitwise module with bitwise_and, bitwise_or, bitwise_xor, and invert
    functions.
*   Add fixed-grid ODE integration routines.
*   Allow passing bounds to ScipyOptimizerInterface.