message: Done with changes as requested
diff: @@ -12,17 +12,24 @@ 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 [...].
  -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
  -```
  +tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype
  +(e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()
  +gives module error.
  For example,
  - tf.bitcast(tf.cast(my_tensor, tf.int8), tf.uint8)
  +Example 1:
  +>>> import tensorflow as tf
  +>>> a = [1., 2., 3.]
  +>>> euality_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:
  +>>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
  +<tf.Tensor: ... shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>
  -Other example,
  +Example 3:
  >>> import tensorflow as tf
  >>> x = [1., 2., 3.]
  >>> y = [0., 2., 3.]
  