message: Done completely
diff: @@ -16,19 +16,23 @@ tf.bitcast() and tf.cast() work differently when real dtype is casted as a compl
  (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()
  gives module error.
  For example,
  +
  Example 1:
  +```python
  >>> a = [1., 2., 3.]
  >>> equality_bitcast = tf.bitcast(a,tf.complex128)
  tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot bitcast from float to complex128: shape [3] [Op:Bitcast]
  >>> equality_cast = tf.cast(a,tf.complex128)
  >>> print(equality_cast)
  tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)
  -
  +```
  Example 2:
  +```python
  >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
  <tf.Tensor: ... shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>
  -
  +```
  Example 3:
  +```python
  >>> x = [1., 2., 3.]
  >>> y = [0., 2., 3.]
  >>> equality= tf.equal(x,y)
  @@ -43,6 +47,7 @@ tf.Tensor(
  [[ 0 0 0 0]
  [ 0 0 128 63]
  [ 0 0 128 63]], shape=(3, 4), dtype=uint8)
  +```
  *NOTE*: Bitcast is implemented as a low-level cast, so machines with different
  endian orderings will give different results.
  