parameters: (values, threshold, weights=None,
                       metrics_collections=None,
                       updates_collections=None,
                       name=None)
return_statement: return mean(is_below_threshold,
                weights,
                metrics_collections,
                updates_collections,
                name or 'percentage_below_threshold')
docstring: Computes the percentage of values less than the given threshold.
  
    The `percentage_below` function creates two local variables,
    `total` and `count` that are used to compute the percentage of `values` that
    fall below `threshold`. This rate is weighted by `weights`, and it is
    ultimately returned as `percentage` which is an idempotent operation that
    simply divides `total` by `count`.
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `percentage`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      values: A numeric `Tensor` of arbitrary size.
      threshold: A scalar threshold.
      weights: Optional `Tensor` whose rank is either 0, or the same rank as
        `values`, and must be broadcastable to `values` (i.e., all dimensions must
        be either `1`, or the same as the corresponding `values` dimension).
      metrics_collections: An optional list of collections that the metric
        value variable should be added to.
      updates_collections: An optional list of collections that the metric update
        ops should be added to.
      name: An optional variable_scope name.
  
    Returns:
      percentage: A `Tensor` representing the current mean, the value of `total`
        divided by `count`.
      update_op: An operation that increments the `total` and `count` variables
        appropriately.
  
    Raises:
      ValueError: If `weights` is not `None` and its shape doesn't match `values`,
        or if either `metrics_collections` or `updates_collections` are not a list
        or tuple.
docstring_summary: Computes the percentage of values less than the given threshold.
docstring_tokens: Computes the percentage of values less than the given threshold .
function: def percentage_below(values, threshold, weights=None,
                       metrics_collections=None,
                       updates_collections=None,
                       name=None):
    """Computes the percentage of values less than the given threshold.
  
    The `percentage_below` function creates two local variables,
    `total` and `count` that are used to compute the percentage of `values` that
    fall below `threshold`. This rate is weighted by `weights`, and it is
    ultimately returned as `percentage` which is an idempotent operation that
    simply divides `total` by `count`.
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `percentage`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      values: A numeric `Tensor` of arbitrary size.
      threshold: A scalar threshold.
      weights: Optional `Tensor` whose rank is either 0, or the same rank as
        `values`, and must be broadcastable to `values` (i.e., all dimensions must
        be either `1`, or the same as the corresponding `values` dimension).
      metrics_collections: An optional list of collections that the metric
        value variable should be added to.
      updates_collections: An optional list of collections that the metric update
        ops should be added to.
      name: An optional variable_scope name.
  
    Returns:
      percentage: A `Tensor` representing the current mean, the value of `total`
        divided by `count`.
      update_op: An operation that increments the `total` and `count` variables
        appropriately.
  
    Raises:
      ValueError: If `weights` is not `None` and its shape doesn't match `values`,
        or if either `metrics_collections` or `updates_collections` are not a list
        or tuple.
    """
    is_below_threshold = math_ops.to_float(math_ops.less(values, threshold))
    return mean(is_below_threshold,
                weights,
                metrics_collections,
                updates_collections,
                name or 'percentage_below_threshold')
function_tokens: def percentage_below ( values , threshold , weights = None , metrics_collections = None , updates_collections = None , name = None ) : is_below_threshold = math_ops . to_float ( math_ops . less ( values , threshold ) ) return mean ( is_below_threshold , weights , metrics_collections , updates_collections , name or 'percentage_below_threshold' )