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Counts the number of occurrences of each value in an integer array `arr`

.

```
tfp.experimental.substrates.numpy.stats.count_integers(
arr, weights=None, minlength=None, maxlength=None, axis=None, dtype=tf.int32,
name=None
)
```

Works like `tf.math.bincount`

, but provides an `axis`

kwarg that specifies
dimensions to reduce over. With
`~axis = [i for i in range(arr.ndim) if i not in axis]`

,
this function returns a `Tensor`

of shape `[K] + arr.shape[~axis]`

.

If `minlength`

and `maxlength`

are not given, `K = tf.reduce_max(arr) + 1`

if `arr`

is non-empty, and 0 otherwise.
If `weights`

are non-None, then index `i`

of the output stores the sum of the
value in `weights`

at each index where the corresponding value in `arr`

is
`i`

.

#### Args:

: An`arr`

`int32`

`Tensor`

of non-negative values.: If non-None, must be the same shape as arr. For each value in`weights`

`arr`

, the bin will be incremented by the corresponding weight instead of 1.: If given, ensures the output has length at least`minlength`

`minlength`

, padding with zeros at the end if necessary.: If given, skips values in`maxlength`

`arr`

that are equal or greater than`maxlength`

, ensuring that the output has length at most`maxlength`

.: A`axis`

`0-D`

or`1-D`

`int32`

`Tensor`

(with static values) designating dimensions in`arr`

to reduce over.`Default value:`

`None`

, meaning reduce over all dimensions.: If`dtype`

`weights`

is None, determines the type of the output bins.: A name scope for the associated operations (optional).`name`

#### Returns:

A vector with the same dtype as `weights`

or the given `dtype`

. The bin
values.