parameters: (predictions, labels, weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None)
return_statement: return metrics.mean_squared_error(
        predictions=predictions, labels=labels, weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections, name=name)
docstring: Computes the mean squared error between the labels and predictions.
  
    The `streaming_mean_squared_error` function creates two local variables,
    `total` and `count` that are used to compute the mean squared error.
    This average is weighted by `weights`, and it is ultimately returned as
    `mean_squared_error`: an idempotent operation that simply divides `total` by
    `count`.
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `mean_squared_error`. Internally, a `squared_error` operation computes the
    element-wise square of the difference between `predictions` and `labels`. Then
    `update_op` increments `total` with the reduced sum of the product of
    `weights` and `squared_error`, and it increments `count` with the reduced sum
    of `weights`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: A `Tensor` of arbitrary shape.
      labels: A `Tensor` of the same shape as `predictions`.
      weights: Optional `Tensor` indicating the frequency with which an example is
        sampled. Rank must be 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
        `mean_squared_error` 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:
      mean_squared_error: A `Tensor` representing the current mean, the value of
        `total` divided by `count`.
      update_op: An operation that increments the `total` and `count` variables
        appropriately and whose value matches `mean_squared_error`.
  
    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: Computes the mean squared error between the labels and predictions.
docstring_tokens: Computes the mean squared error between the labels and predictions .
function: def streaming_mean_squared_error(predictions, labels, weights=None,
                                   metrics_collections=None,
                                   updates_collections=None,
                                   name=None):
    """Computes the mean squared error between the labels and predictions.
  
    The `streaming_mean_squared_error` function creates two local variables,
    `total` and `count` that are used to compute the mean squared error.
    This average is weighted by `weights`, and it is ultimately returned as
    `mean_squared_error`: an idempotent operation that simply divides `total` by
    `count`.
  
    For estimation of the metric over a stream of data, the function creates an
    `update_op` operation that updates these variables and returns the
    `mean_squared_error`. Internally, a `squared_error` operation computes the
    element-wise square of the difference between `predictions` and `labels`. Then
    `update_op` increments `total` with the reduced sum of the product of
    `weights` and `squared_error`, and it increments `count` with the reduced sum
    of `weights`.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      predictions: A `Tensor` of arbitrary shape.
      labels: A `Tensor` of the same shape as `predictions`.
      weights: Optional `Tensor` indicating the frequency with which an example is
        sampled. Rank must be 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
        `mean_squared_error` 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:
      mean_squared_error: A `Tensor` representing the current mean, the value of
        `total` divided by `count`.
      update_op: An operation that increments the `total` and `count` variables
        appropriately and whose value matches `mean_squared_error`.
  
    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.
    """
    return metrics.mean_squared_error(
        predictions=predictions, labels=labels, weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections, name=name)
function_tokens: def streaming_mean_squared_error ( predictions , labels , weights = None , metrics_collections = None , updates_collections = None , name = None ) : return metrics . mean_squared_error ( predictions = predictions , labels = labels , weights = weights , metrics_collections = metrics_collections , updates_collections = updates_collections , name = name )