BUG: np.min(pd.Index) and np.max(pd.Index) raise a TypeError
#### Code Sample, a copy-pastable example if possible

```python
In [1]: import numpy as np; import pandas as pd

In [2]: np.__version__, pd.__version__
Out[2]: ('1.16.2', '0.25.0.dev0+421.g8b60ebb359')

In [3]: idx = pd.Index([10, 20, 11]); idx
Out[3]: Int64Index([10, 20, 11], dtype='int64')

In [4]: np.min(idx)
---------------------------------------------------------------------------
TypeError: min() got an unexpected keyword argument 'out'

In [5]: np.max(idx)
---------------------------------------------------------------------------
TypeError: max() got an unexpected keyword argument 'out'
```
Note that other numpy functions work fine:
```python
In [6]: np.sum(idx), np.prod(idx), np.argmax(idx), np.argmin(idx)
Out[6]: (41, 2200, 1, 0)
```
And `np.min` and `np.max` work on other pandas data structures:
```python
In [7]: s = pd.Series([10, 20, 11])

In [8]: np.min(s), np.max(s)
Out[8]: (10, 20)

In [9]: df = pd.DataFrame({'A': [10, 20, 11], 'B': [3, 1, 4]})

In [10]: np.min(df)
Out[10]:
A    10
B     1
dtype: int64

In [11]: np.max(df)
Out[11]:
A    20
B     4
dtype: int64
```

#### Problem description
Both `np.min` and `np.max` raise a `TypeError` when applied to a `pd.Index`.  Using `pd.Index.min` and `pd.Index.max` is more idiomatic for pandas, but using the numpy equivalent functions should work, as they do for other pandas data structures.

#### Expected Output
I'd expect `np.min(pd.Index)` and `np.max(pd.Index)` to be equivalent to `pd.Index.min` and `pd.Index.max`, as is the case with other pandas data structures.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: 8b60ebb3598881571b390276d2c75ec7c7a95212
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.25.0.dev0+421.g8b60ebb359
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.6.3
Cython: 0.28.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.6.0
xarray: 0.9.6
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml.etree: 3.8.0
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
