parameters: (values, weights=None, metrics_collections=None,
                     updates_collections=None, name=None)
return_statement: return metrics.mean(
        values=values, weights=weights, metrics_collections=metrics_collections,
        updates_collections=updates_collections, name=name)
docstring: Computes the (weighted) mean of the given values.
  
    The `streaming_mean` function creates two local variables, `total` and `count`
    that are used to compute the average of `values`. This average is ultimately
    returned as `mean` 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 `mean`.
    `update_op` increments `total` with the reduced sum of the product of `values`
    and `weights`, and it increments `count` with the reduced sum of `weights`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      values: A `Tensor` of arbitrary dimensions.
      weights: `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 `mean`
        should be added to.
      updates_collections: An optional list of collections that `update_op`
        should be added to.
      name: An optional variable_scope name.
  
    Returns:
      mean: 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 and whose value matches `mean`.
  
    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 (weighted) mean of the given values.
docstring_tokens: Computes the ( weighted ) mean of the given values .
function: def streaming_mean(values, weights=None, metrics_collections=None,
                     updates_collections=None, name=None):
    """Computes the (weighted) mean of the given values.
  
    The `streaming_mean` function creates two local variables, `total` and `count`
    that are used to compute the average of `values`. This average is ultimately
    returned as `mean` 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 `mean`.
    `update_op` increments `total` with the reduced sum of the product of `values`
    and `weights`, and it increments `count` with the reduced sum of `weights`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      values: A `Tensor` of arbitrary dimensions.
      weights: `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 `mean`
        should be added to.
      updates_collections: An optional list of collections that `update_op`
        should be added to.
      name: An optional variable_scope name.
  
    Returns:
      mean: 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 and whose value matches `mean`.
  
    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.
    """
    return metrics.mean(
        values=values, weights=weights, metrics_collections=metrics_collections,
        updates_collections=updates_collections, name=name)
function_tokens: def streaming_mean ( values , weights = None , metrics_collections = None , updates_collections = None , name = None ) : return metrics . mean ( values = values , weights = weights , metrics_collections = metrics_collections , updates_collections = updates_collections , name = name )