parameters: (predictions,
                                   labels,
                                   k,
                                   class_id=None,
                                   weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None)
return_statement: return metrics.recall_at_k(
        k=k,
        class_id=class_id,
        predictions=predictions,
        labels=labels,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=name)
docstring: Computes recall@k of the predictions with respect to sparse labels.
  
    If `class_id` is not specified, we'll calculate recall as the ratio of true
        positives (i.e., correct predictions, items in the top `k` highest
        `predictions` that are found in the corresponding row in `labels`) to
        actual positives (the full `labels` row).
    If `class_id` is specified, we calculate recall by considering only the rows
        in the batch for which `class_id` is in `labels`, and computing the
        fraction of them for which `class_id` is in the corresponding row in
        `labels`.
  
    `streaming_sparse_recall_at_k` creates two local variables,
    `true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
    the recall_at_k frequency. This frequency is ultimately returned as
    `recall_at_<k>`: an idempotent operation that simply divides
    `true_positive_at_<k>` by total (`true_positive_at_<k>` +
    `false_negative_at_<k>`).
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `recall_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
    indicating the top `k` `predictions`. Set operations applied to `top_k` and
    `labels` calculate the true positives and false negatives weighted by
    `weights`. Then `update_op` increments `true_positive_at_<k>` and
    `false_negative_at_<k>` using these values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where N >=
        1. Commonly, N=1 and predictions has shape [batch size, num_classes]. The
        final dimension contains the logit values for each class. [D1, ... DN]
        must match `labels`.
      labels: `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN,
        num_labels], where N >= 1 and num_labels is the number of target classes
        for the associated prediction. Commonly, N=1 and `labels` has shape
        [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values
        should be in range [0, num_classes), where num_classes is the last
        dimension of `predictions`. Values outside this range always count towards
        `false_negative_at_<k>`.
      k: Integer, k for @k metric.
      class_id: Integer class ID for which we want binary metrics. This should be
        in range [0, num_classes), where num_classes is the last dimension of
        `predictions`. If class_id is outside this range, the method returns NAN.
      weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
        `labels`. If the latter, it 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 values should be
        added to.
      updates_collections: An optional list of collections that updates should be
        added to.
      name: Name of new update operation, and namespace for other dependent ops.
  
    Returns:
      recall: Scalar `float64` `Tensor` with the value of `true_positives` divided
        by the sum of `true_positives` and `false_negatives`.
      update_op: `Operation` that increments `true_positives` and
        `false_negatives` variables appropriately, and whose value matches
        `recall`.
  
    Raises:
      ValueError: 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: Computes recall@k of the predictions with respect to sparse labels.
docstring_tokens: Computes recall@k of the predictions with respect to sparse labels .
function: def streaming_sparse_recall_at_k(predictions,
                                   labels,
                                   k,
                                   class_id=None,
                                   weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None):
    """Computes recall@k of the predictions with respect to sparse labels.
  
    If `class_id` is not specified, we'll calculate recall as the ratio of true
        positives (i.e., correct predictions, items in the top `k` highest
        `predictions` that are found in the corresponding row in `labels`) to
        actual positives (the full `labels` row).
    If `class_id` is specified, we calculate recall by considering only the rows
        in the batch for which `class_id` is in `labels`, and computing the
        fraction of them for which `class_id` is in the corresponding row in
        `labels`.
  
    `streaming_sparse_recall_at_k` creates two local variables,
    `true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
    the recall_at_k frequency. This frequency is ultimately returned as
    `recall_at_<k>`: an idempotent operation that simply divides
    `true_positive_at_<k>` by total (`true_positive_at_<k>` +
    `false_negative_at_<k>`).
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `recall_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
    indicating the top `k` `predictions`. Set operations applied to `top_k` and
    `labels` calculate the true positives and false negatives weighted by
    `weights`. Then `update_op` increments `true_positive_at_<k>` and
    `false_negative_at_<k>` using these values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where N >=
        1. Commonly, N=1 and predictions has shape [batch size, num_classes]. The
        final dimension contains the logit values for each class. [D1, ... DN]
        must match `labels`.
      labels: `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN,
        num_labels], where N >= 1 and num_labels is the number of target classes
        for the associated prediction. Commonly, N=1 and `labels` has shape
        [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values
        should be in range [0, num_classes), where num_classes is the last
        dimension of `predictions`. Values outside this range always count towards
        `false_negative_at_<k>`.
      k: Integer, k for @k metric.
      class_id: Integer class ID for which we want binary metrics. This should be
        in range [0, num_classes), where num_classes is the last dimension of
        `predictions`. If class_id is outside this range, the method returns NAN.
      weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
        `labels`. If the latter, it 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 values should be
        added to.
      updates_collections: An optional list of collections that updates should be
        added to.
      name: Name of new update operation, and namespace for other dependent ops.
  
    Returns:
      recall: Scalar `float64` `Tensor` with the value of `true_positives` divided
        by the sum of `true_positives` and `false_negatives`.
      update_op: `Operation` that increments `true_positives` and
        `false_negatives` variables appropriately, and whose value matches
        `recall`.
  
    Raises:
      ValueError: 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.
    """
    return metrics.recall_at_k(
        k=k,
        class_id=class_id,
        predictions=predictions,
        labels=labels,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=name)
function_tokens: def streaming_sparse_recall_at_k ( predictions , labels , k , class_id = None , weights = None , metrics_collections = None , updates_collections = None , name = None ) : return metrics . recall_at_k ( k = k , class_id = class_id , predictions = predictions , labels = labels , weights = weights , metrics_collections = metrics_collections , updates_collections = updates_collections , name = name )