parameters: (predictions, labels, weights=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None)
docstring: Sum the weights of true_positives.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: The predicted values, a `bool` `Tensor` of arbitrary
        dimensions.
      labels: The ground truth values, a `bool` `Tensor` whose dimensions must
        match `predictions`.
      weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
      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:
      value_tensor: A tensor representing the current value of the metric.
      update_op: An operation that accumulates the error from a batch of data.
  
    Raises:
      ValueError: If `predictions` and `labels` have mismatched shapes, or if
        `weights` is not `None` and its shape doesn't match `predictions`, or if
        either `metrics_collections` or `updates_collections` are not a list or
        tuple.
docstring_summary: Sum the weights of true_positives.
docstring_tokens: Sum the weights of true_positives .
function: def _streaming_true_positives(predictions, labels, weights=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None):
    """Sum the weights of true_positives.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: The predicted values, a `bool` `Tensor` of arbitrary
        dimensions.
      labels: The ground truth values, a `bool` `Tensor` whose dimensions must
        match `predictions`.
      weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
      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:
      value_tensor: A tensor representing the current value of the metric.
      update_op: An operation that accumulates the error from a batch of data.
  
    Raises:
      ValueError: If `predictions` and `labels` have mismatched shapes, or if
        `weights` is not `None` and its shape doesn't match `predictions`, or if
        either `metrics_collections` or `updates_collections` are not a list or
        tuple.
    """
    with variable_scope.variable_scope(
        name, 'true_positives', [predictions, labels]):
  
      predictions.get_shape().assert_is_compatible_with(labels.get_shape())
      is_true_positive = math_ops.logical_and(math_ops.equal(labels, 1),
                                              math_ops.equal(predictions, 1))
      return _count_condition(is_true_positive, weights, metrics_collections,
                              updates_collections)
function_tokens: def _streaming_true_positives ( predictions , labels , weights = None , metrics_collections = None , updates_collections = None , name = None ) : with variable_scope . variable_scope ( name , 'true_positives' , [ predictions , labels ] ) : predictions . get_shape ( ) . assert_is_compatible_with ( labels . get_shape ( ) ) is_true_positive = math_ops . logical_and ( math_ops . equal ( labels , 1 ) , math_ops . equal ( predictions , 1 ) ) return _count_condition ( is_true_positive , weights , metrics_collections , updates_collections )