parameters: (predictions, labels, weights=None,
                               metrics_collections=None,
                               updates_collections=None,
                               name=None)
docstring: Sum the weights of true_negatives.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
        be cast to `bool`.
      labels: The ground truth values, a `Tensor` whose dimensions must match
        `predictions`. Will be cast to `bool`.
      weights: Optional `Tensor` whose rank is either 0, or the same rank as
        `labels`, and must be broadcastable to `labels` (i.e., all dimensions
        must be either `1`, or the same as the corresponding `labels`
        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:
      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_negatives.
docstring_tokens: Sum the weights of true_negatives .
function: def streaming_true_negatives(predictions, labels, weights=None,
                               metrics_collections=None,
                               updates_collections=None,
                               name=None):
    """Sum the weights of true_negatives.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
        be cast to `bool`.
      labels: The ground truth values, a `Tensor` whose dimensions must match
        `predictions`. Will be cast to `bool`.
      weights: Optional `Tensor` whose rank is either 0, or the same rank as
        `labels`, and must be broadcastable to `labels` (i.e., all dimensions
        must be either `1`, or the same as the corresponding `labels`
        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:
      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_negatives', (predictions, labels, weights)):
  
      predictions, labels, weights = _remove_squeezable_dimensions(
          predictions=math_ops.cast(predictions, dtype=dtypes.bool),
          labels=math_ops.cast(labels, dtype=dtypes.bool),
          weights=weights)
      is_true_negative = math_ops.logical_and(math_ops.equal(labels, False),
                                              math_ops.equal(predictions, False))
      return _count_condition(is_true_negative, weights, metrics_collections,
                              updates_collections)
function_tokens: def streaming_true_negatives ( predictions , labels , weights = None , metrics_collections = None , updates_collections = None , name = None ) : with variable_scope . variable_scope ( name , 'true_negatives' , ( predictions , labels , weights ) ) : predictions , labels , weights = _remove_squeezable_dimensions ( predictions = math_ops . cast ( predictions , dtype = dtypes . bool ) , labels = math_ops . cast ( labels , dtype = dtypes . bool ) , weights = weights ) is_true_negative = math_ops . logical_and ( math_ops . equal ( labels , False ) , math_ops . equal ( predictions , False ) ) return _count_condition ( is_true_negative , weights , metrics_collections , updates_collections )