DOC: update the IntervalIndex.from_array docstring
State: closed

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```

################################################################################
################# Docstring (pandas.IntervalIndex.from_arrays) #################
################################################################################

Construct an IntervalIndex from a given element in a left
and right array.

Parameters
----------
left : array-like (1-dimensional)
    Left bounds for each interval.
right : array-like (1-dimensional)
    Right bounds for each interval.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
    Whether the intervals are closed on the left-side, right-side, both
    or neither.
name : object, optional
    Name to be stored in the index.
copy : boolean, default False
    Copy the data.
dtype : dtype or None, default None
    If None, dtype will be inferred.

    .. versionadded:: 0.23.0.

Returns
-------
index : IntervalIndex

Examples
--------
>>> pd.IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])
IntervalIndex([(0, 1], (1, 2], (2, 3]]
              closed='right',
              dtype='interval[int64]')

If you want to segment different groups of people based on
ages, you can apply the method as follows:

>>> ages = pd.IntervalIndex.from_arrays([0, 2, 13],
...                                     [2, 13, 19], closed='left')
>>> ages
IntervalIndex([[0, 2), [2, 13), [13, 19)]
      closed='left',
      dtype='interval[int64]')
>>> s = pd.Series(['baby', 'kid', 'teen'], ages)
>>> s
[0, 2)      baby
[2, 13)      kid
[13, 19)    teen
dtype: object

Notes
-----
Each element of `left` must be smaller or equal to the `right` element
at the same position, ie, ``left[i] <= right[i]``.

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
                            splits.
IntervalIndex.from_tuples : Construct an IntervalIndex from a
                            list/array of tuples.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	No summary found (a short summary in a single line should be present at the beginning of the docstring)

```

Labels:
name: Docs
name: Interval
description: Interval data type

Comments:
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| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/20224?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/indexes/interval.py](https://codecov.io/gh/pandas-dev/pandas/pull/20224/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhlcy9pbnRlcnZhbC5weQ==) | `93.08% <ø> (ø)` | |

------

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Thanks @verakai!