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    %0 = "tf.B"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
    %1 = "tf.D"(%0) {array_attr_funcs = [@referenced_func0, @referenced_func1]} : (tensor<?xi32>) -> tensor<?xi32>
    %2 = "tf.E"(%1) {dictionary_attr_funcs = {fn1 = @referenced_func2, fn2 = @referenced_func3}} : (tensor<?xi32>) -> tensor<?xi32>
    func.return %0 : tensor<?xi32>
  }

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	y += x
	return (y-1)&y == 0
}

// bitconEncode returns the encoding of a bitcon used in logical instructions
// x is known to be a bitcon
// a bitcon is a sequence of n ones at low bits (i.e. 1<<n-1), right rotated
// by R bits, and repeated with period of 64, 32, 16, 8, 4, or 2.
// it is encoded in logical instructions with 3 bitfields
// N (1 bit) : R (6 bits) : S (6 bits), where
// N=1           -- period=64

  For complex numbers, the exponential value is calculated as follows:

  ```
  e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y)
  ```

  Let's consider complex number 1+1j as an example.
  e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j)

  ```python
  x = tf.constant(1 + 1j)
  tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j
  ```
  }];