message: Gradient Doc Changed for v2
diff: @@ -190,7 +190,7 @@ def gradients_v2(ys, # pylint: disable=invalid-name
  `gradients()` adds ops to the graph to output the derivatives of `ys` with
  respect to `xs`. It returns a list of `Tensor` of length `len(xs)` where
  - each tensor is the `sum(dy/dx)` for y in `ys`.
  + each tensor is the `sum(dy/dx)` for y in `ys` and for x in `xs`.
  `grad_ys` is a list of tensors of the same length as `ys` that holds
  the initial gradients for each y in `ys`. When `grad_ys` is None,
  @@ -248,6 +248,19 @@ def gradients_v2(ys, # pylint: disable=invalid-name
  sess.run(g2) # [array([[0., 0.]], dtype=float32)]
  ```
  + Let us take one practical example which comes during the back propogation
  + phase. This function is used to evaluate the derivatives of the cost function
  + with respect to Weights `Ws` and Biases `bs`. Below sample implementation
  + provides the exaplantion of what it is actually used for :
  +
  + ```python
  + Ws = tf.constant(0.)
  + bs = 2 * Ws
  + cost = Ws + bs # This is just an example. So, please ignore the formulas.
  + g = tf.gradients(cost, [Ws, bs])
  + dCost_dW, dCost_db = g
  + ```
  +
  Args:
  ys: A `Tensor` or list of tensors to be differentiated.
  @@ -268,7 +281,8 @@ def gradients_v2(ys, # pylint: disable=invalid-name
  `none`.
  Returns:
  - A list of `sum(dy/dx)` for each x in `xs`.
  + A list of `Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`
  + for y in `ys` and for x in `xs`.
  Raises:
  LookupError: if one of the operations between `x` and `y` does not
  