message: Fix some warnings with keep_dims in `tf.contrib.distributions`
  This fix fixes some warnings with keep_dims in `tf.contrib.distributions`
diff: @@ -1041,14 +1041,14 @@ def reduce_weighted_logsumexp(
  with ops.name_scope(name, "reduce_weighted_logsumexp", [logx, w]):
  logx = ops.convert_to_tensor(logx, name="logx")
  if w is None:
  - lswe = math_ops.reduce_logsumexp(logx, axis=axis, keep_dims=keep_dims)
  + lswe = math_ops.reduce_logsumexp(logx, axis=axis, keepdims=keep_dims)
  if return_sign:
  sgn = array_ops.ones_like(lswe)
  return lswe, sgn
  return lswe
  w = ops.convert_to_tensor(w, dtype=logx.dtype, name="w")
  log_absw_x = logx + math_ops.log(math_ops.abs(w))
  - max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keep_dims=True)
  + max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keepdims=True)
  # If the largest element is `-inf` or `inf` then we don't bother subtracting
  # off the max. We do this because otherwise we'd get `inf - inf = NaN`. That
  # this is ok follows from the fact that we're actually free to subtract any
  @@ -1062,7 +1062,7 @@ def reduce_weighted_logsumexp(
  sum_wx_over_max_absw_x = math_ops.reduce_sum(
  wx_over_max_absw_x,
  axis=axis,
  - keep_dims=keep_dims)
  + keepdims=keep_dims)
  if not keep_dims:
  max_log_absw_x = array_ops.squeeze(max_log_absw_x, axis)
  sgn = math_ops.sign(sum_wx_over_max_absw_x)
  @@ -1180,7 +1180,7 @@ def process_quadrature_grid_and_probs(
  grid = ops.convert_to_tensor(grid, name="grid", dtype=dtype)
  probs = ops.convert_to_tensor(probs, name="unnormalized_probs",
  dtype=dtype)
  - probs /= linalg_ops.norm(probs, ord=1, axis=-1, keep_dims=True,
  + probs /= linalg_ops.norm(probs, ord=1, axis=-1, keepdims=True,
  name="probs")
  def _static_event_size(x):
  