%0 = "tf.BitwiseXor"(%arg0, %arg1) : (tensor<4xui32>, tensor<4xui32>) -> tensor<4xui32>
  func.return %0: tensor<4xui32>
}

// CHECK-LABEL: func @bitwise_and
func.func @bitwise_and(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<4xi32> {
  // CHECK-NEXT: mhlo.and
  %0 = "tf.BitwiseAnd"(%arg0, %arg1) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
  func.return %0: tensor<4xi32>
}

// CHECK-LABEL: func @bitwise_and_unsigned

//
// if value.shape[1] % 2:
//   value = pad(value, [0, 1, 0, 0])
//
// slice_shape = value.shape
// slice_shape[1] /= 2
//
// packed_low = slice(value, [0, 0, 0, 0], slice_shape)
// packed_low = bitwise_and(packed_low, 0x0F)
//
// packed_high = slice(value, [0, value.shape[1] / 2, 0, 0], slice_shape)
// packed_high = left_shift(packed_high, 4)
//
// packed_value = bitwise_or(packed_low, packed_high)

  %1 = mhlo.xor %0, %arg1 : tensor<1x4xi8>
  func.return %1 : tensor<1x4xi8>
}

// CHECK-LABEL:   func @bitwise_and(
// CHECK-SAME:                      %[[VAL_0:.*]]: tensor<4xi32>,
// CHECK-SAME:                      %[[VAL_1:.*]]: tensor<4xi32>) -> tensor<4xi32> {
// CHECK:           %[[VAL_2:.*]] = "tf.BitwiseAnd"(%[[VAL_0]], %[[VAL_1]]) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
// CHECK:           return %[[VAL_2]] : tensor<4xi32>

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.

            // Binary
            "Add", "AddV2", "Sub", "Mul", "Div", "Atan2", "Complex", "DivNoNan",
            "MulNoNan", "FloorDiv", "Xlogy", "Xlog1py", "Xdivy", "FloorMod",
            "BitwiseAnd", "BitwiseOr", "BitwiseXor", "LeftShift", "RightShift",
            "LogicalAnd", "LogicalOr", "Mod", "Maximum", "Minimum", "RealDiv",
            "ReciprocalGrad", "RsqrtGrad", "SqrtGrad", "TruncateDiv",