message: Reset batchnorm to be the same as the master
diff: @@ -430,10 +430,6 @@ class BatchNormalization(Layer):
  stddev = math_ops.sqrt(variance + self.epsilon)
  # Compute the average mean and standard deviation, as if they were
  # initialized with this batch's moments.
  - param_dtype = self.renorm_mean.dtype
  - mean = math_ops.cast(mean, param_dtype)
  - stddev = math_ops.cast(stddev, param_dtype)
  -
  mixed_renorm_mean = (self.renorm_mean +
  (1. - self.renorm_mean_weight) * mean)
  mixed_renorm_stddev = (self.renorm_stddev +
  @@ -448,13 +444,10 @@ class BatchNormalization(Layer):
  rmin, rmax, dmax = [self.renorm_clipping.get(key)
  for key in ['rmin', 'rmax', 'dmax']]
  if rmin is not None:
  - rmin = math_ops.cast(rmin, param_dtype)
  r = math_ops.maximum(r, rmin)
  if rmax is not None:
  - rmax = math_ops.cast(rmax, param_dtype)
  r = math_ops.minimum(r, rmax)
  if dmax is not None:
  - dmax = math_ops.cast(dmax, param_dtype)
  d = math_ops.maximum(d, -dmax)
  d = math_ops.minimum(d, dmax)
  # When not training, use r=1, d=0.
  @@ -630,8 +623,6 @@ class BatchNormalization(Layer):
  variance = math_ops.cast(variance, inputs.dtype)
  if offset is not None:
  offset = math_ops.cast(offset, inputs.dtype)
  - if scale is not None:
  - scale = math_ops.cast(scale, inputs.dtype)
  outputs = nn.batch_normalization(inputs,
  _broadcast(mean),
  _broadcast(variance),
  