message: Final correction
diff: @@ -12,9 +12,32 @@ If `T` is smaller than `type`, the operator requires that the rightmost
  dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
  [..., sizeof(`type`)/sizeof(`T`)] to [...].
  -In case ofconverting from a real data type to complex data type(e.g. tf.complex64,
  -tf.complex128) tf.bitcast() give error while tf.cast() make imaginary part of output
  -0.
  +tf.bitcast() is used when you have to cast to an unsigned dtype (e.g. tf.uint8)
  +where tf.cast() will not work effectively.
  +
  +```
  +python
  +code here
  +```
  +For example,
  + tf.bitcast(tf.cast(my_tensor, tf.int8), tf.uint8)
  +
  +Other example,
  +>>> import tensorflow as tf
  +>>> x = [1., 2., 3.]
  +>>> y = [0., 2., 3.]
  +>>> equality= tf.equal(x,y)
  +>>> equality_cast = tf.cast(equality,tf.float32)
  +>>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8)
  +>>> print(equality)
  +tf.Tensor([False True True], shape=(3,), dtype=bool)
  +>>> print(equality_cast)
  +tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
  +>>> print(equality_bitcast)
  +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.
  