parameters: (labels,
                                   predictions,
                                   thresholds,
                                   weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None)
docstring: Computes true negatives at provided threshold values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      labels: A `Tensor` whose shape matches `predictions`. Will be cast to
        `bool`.
      predictions: A floating point `Tensor` of arbitrary shape and whose values
        are in the range `[0, 1]`.
      thresholds: A python list or tuple of float thresholds in `[0, 1]`.
      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 `true_negatives`
        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:
      true_negatives:  A float `Tensor` of shape `[len(thresholds)]`.
      update_op: An operation that updates the `true_negatives` variable and
        returns its current value.
  
    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.
      RuntimeError: If eager execution is enabled.
docstring_summary: Computes true negatives at provided threshold values.
docstring_tokens: Computes true negatives at provided threshold values .
function: def true_negatives_at_thresholds(labels,
                                   predictions,
                                   thresholds,
                                   weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None):
    """Computes true negatives at provided threshold values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      labels: A `Tensor` whose shape matches `predictions`. Will be cast to
        `bool`.
      predictions: A floating point `Tensor` of arbitrary shape and whose values
        are in the range `[0, 1]`.
      thresholds: A python list or tuple of float thresholds in `[0, 1]`.
      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 `true_negatives`
        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:
      true_negatives:  A float `Tensor` of shape `[len(thresholds)]`.
      update_op: An operation that updates the `true_negatives` variable and
        returns its current value.
  
    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.
      RuntimeError: If eager execution is enabled.
    """
    if context.executing_eagerly():
      raise RuntimeError('tf.metrics.true_negatives_at_thresholds is not '
                         'supported when eager execution is enabled.')
  
    with variable_scope.variable_scope(name, 'true_negatives',
                                       (predictions, labels, weights)):
      values, update_ops = _confusion_matrix_at_thresholds(
          labels, predictions, thresholds, weights=weights, includes=('tn',))
  
      tn_value = _aggregate_variable(values['tn'], metrics_collections)
  
      if updates_collections:
        ops.add_to_collections(updates_collections, update_ops['tn'])
  
      return tn_value, update_ops['tn']
function_tokens: def true_negatives_at_thresholds ( labels , predictions , thresholds , weights = None , metrics_collections = None , updates_collections = None , name = None ) : if context . executing_eagerly ( ) : raise RuntimeError ( 'tf.metrics.true_negatives_at_thresholds is not ' 'supported when eager execution is enabled.' ) with variable_scope . variable_scope ( name , 'true_negatives' , ( predictions , labels , weights ) ) : values , update_ops = _confusion_matrix_at_thresholds ( labels , predictions , thresholds , weights = weights , includes = ( 'tn' , ) ) tn_value = _aggregate_variable ( values [ 'tn' ] , metrics_collections ) if updates_collections : ops . add_to_collections ( updates_collections , update_ops [ 'tn' ] ) return tn_value , update_ops [ 'tn' ]