# tf.nn.sufficient_statistics

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_impl.py#L1263-L1290) |

Calculate the sufficient statistics for the mean and variance of `x`.

    tf.nn.sufficient_statistics(
        x, axes, shift=None, keepdims=False, name=None
    )

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>

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| ## Args ||
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| `x` | A `Tensor`. |
| `axes` | Array of ints. Axes along which to compute mean and variance. |
| `shift` | 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. |
| `keepdims` | produce statistics with the same dimensionality as the input. |
| `name` | Name used to scope the operations that compute the sufficient stats. |

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| ## Returns ||
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| Four `Tensor` objects of the same type as `x`: <br /> - 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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