message: [XLA] Update operation semantics doc for BatchNorm operations
  Update formulas for BatchNormGrad. The rendered version of the new formulas can be found here:
  Update output table to include the symbol name for each output.
  Fix a typo in BatchNormGrad's input table to correct display the symbol `beta`.
diff: @@ -45,13 +45,16 @@ feature dimension in `operand`), the operation calculates the gradients with
  respect to `operand`, `offset` and `scale` across all the other dimensions. The
  `feature_index` must be a valid index for the feature dimension in `operand`.
  -The three gradients are defined by the following formulas:
  +The three gradients are defined by the following formulas (Assuming a
  +4-dimensional tensor as `operand` and (l) is the index for feature dimension):
  -\\( \nabla x = \nabla y * \gamma * \sqrt{\sigma^2+\epsilon} \\)
  +\\( coef_l = \frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h (\nabla y_{ijkl} * (x_{ijkl} - \mu_l) / (\sigma^2_{l}+\epsilon)) \\)
  -\\( \nabla \gamma = sum(\nabla y * (x - \mu) * \sqrt{\sigma^2 + \epsilon}) \\)
  +\\( \nabla x_{ijkl} = \gamma_{l} * (1/\sqrt{\sigma^2_{l}+\epsilon}) * [\nabla y_{ijkl} - mean(\nabla y) - (x_{ijkl} - \mu_{l}) * coef_l] \\)
  -\\( \nabla \beta = sum(\nabla y) \\)
  +\\( \nabla \beta_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} \\)
  +
  +\\( \nabla \gamma_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} * ((x_{ijkl} - \mu_l) / \sqrt{\sigma^2_{l}+\epsilon}) \\)
  The inputs `mean` and `variance` represents moments value
  across batch and spatial dimensions.
  @@ -61,11 +64,11 @@ The output type is a tuple of three handles:
  |Outputs | Type | Semantics |
  |------------- | ----------------------- | ------------------------------------ |
  |`grad_operand`| `ComputationDataHandle` | gradient with respect to input |
  -: : : `operand` :
  +: : : `operand` (\\( \nabla x\\)) :
  |`grad_scale` | `ComputationDataHandle` | gradient with respect to input |
  -: : : `scale` :
  +: : : `scale` (\\( \nabla \gamma\\)) :
  |`grad_offset` | `ComputationDataHandle` | gradient with respect to input |
  -: : : `offset` :
  +: : : `offset`(\\( \nabla \beta\\)) :
  ## BatchNormInference
  @@ -119,11 +122,11 @@ Normalizes an array across batch and spatial dimensions.
  | Arguments | Type | Semantics |
  | --------------- | ----------------------- | -------------------------------- |
  | `operand` | `ComputationDataHandle` | n dimensional array to be |
  -: : : normalized :
  +: : : normalized (x) :
  | `scale` | `ComputationDataHandle` | 1 dimensional array |
  : : : (\\(\gamma\\)) :
  | `offset` | `ComputationDataHandle` | 1 dimensional array |
  -: : : (\\(\beta\\ ) :
  +: : : (\\(\beta\\)) :
  | `epsilon` | `float` | Epsilon value (\\(\epsilon\\)) |
  | `feature_index` | `int64` | Index to feature dimension |
  : : : in `operand` :
  