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### `tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)` {#sufficient_statistics}
Calculate the sufficient statistics for the mean and variance of `x`.
These sufficient statistics are computed using the one pass algorithm on
an input that's optionally shifted. See:
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
##### Args:
* <b>`x`</b>: A `Tensor`.
* <b>`axes`</b>: Array of ints. Axes along which to compute mean and variance.
* <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
numerical stability, or `None` if no shift is to be performed. A shift
close to the true mean provides the most numerically stable results.
* <b>`keep_dims`</b>: produce statistics with the same dimensionality as the input.
* <b>`name`</b>: Name used to scope the operations that compute the sufficient stats.
##### Returns:
Four `Tensor` objects of the same type as `x`:
* the count (number of elements to average over).
* the (possibly shifted) sum of the elements in the array.
* the (possibly shifted) sum of squares of the elements in the array.
* the shift by which the mean must be corrected or None if `shift` is None.
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