parameters: (predictions,
                                labels,
                                weights=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None)
return_statement: return metrics.false_negatives(
        predictions=predictions,
        labels=labels,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=name)
docstring: Computes the total number of false 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 `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 total number of false negatives.
docstring_tokens: Computes the total number of false negatives .
function: def streaming_false_negatives(predictions,
                                labels,
                                weights=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None):
    """Computes the total number of false 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 `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.false_negatives(
        predictions=predictions,
        labels=labels,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=name)
function_tokens: def streaming_false_negatives ( predictions , labels , weights = None , metrics_collections = None , updates_collections = None , name = None ) : return metrics . false_negatives ( predictions = predictions , labels = labels , weights = weights , metrics_collections = metrics_collections , updates_collections = updates_collections , name = name )