DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
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Please include the output of the validation script below between the "```" ticks:

```
################################################################################
########## Docstring (pandas.core.groupby.DataFrameGroupBy.resample)  ##########
################################################################################

Provide resampling when using a TimeGrouper.

Given a grouper the function resamples it according to a string
"string" -> "frequency".

See the :ref:`frequency aliases <timeseries.offset-aliases>`
documentation for more details.

Parameters
----------
rule : str or Offset
    The offset string or object representing target grouper conversion.
*args, **kwargs : [closed, label, loffset]
    For compatibility with other groupby methods. See below for some
    example parameters.
closed : {‘right’, ‘left’}
    Which side of bin interval is closed.
label : {‘right’, ‘left’}
    Which bin edge label to label bucket with.
loffset : timedelta
    Adjust the resampled time labels.

Returns
-------
Grouper
    Return a new grouper with our resampler appended.

Examples
--------
Start by creating a length-4 DataFrame with minute frequency.

>>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
...                   index=idx,
...                   columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
                     a  b
2000-01-01 00:00:00  0  1
2000-01-01 00:01:00  0  1
2000-01-01 00:02:00  5  1
2000-01-01 00:03:00  0  1

Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.

>>> df.groupby('a').resample('3T').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  2
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:00:00  5  1

Upsample the series into 30 second bins.

>>> df.groupby('a').resample('30S').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:00:30  0  0
    2000-01-01 00:01:00  0  1
    2000-01-01 00:01:30  0  0
    2000-01-01 00:02:00  0  0
    2000-01-01 00:02:30  0  0
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:02:00  5  1

Resample by month. Values are assigned to the month of the period.

>>> df.groupby('a').resample('M').sum()
                a  b
a
0   2000-01-31  0  3
5   2000-01-31  5  1

Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.

>>> df.groupby('a').resample('3T', closed='right').sum()
                         a  b
a
0   1999-12-31 23:57:00  0  1
    2000-01-01 00:00:00  0  2
5   2000-01-01 00:00:00  5  1

Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.

>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:03:00  0  2
5   2000-01-01 00:03:00  5  1

Add an offset of twenty seconds.

>>> df.groupby('a').resample('3T', loffset='20s').sum()
                         a  b
a
0   2000-01-01 00:00:20  0  2
    2000-01-01 00:03:20  0  1
5   2000-01-01 00:00:20  5  1


See also
--------
pandas.Series.groupby
pandas.DataFrame.groupby
pandas.Panel.groupby

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Use only one blank line to separate sections or paragraphs
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'*args, **kwargs', 'loffset', 'label', 'closed'}
```

args and kwargs are described with asterisks as requested, even if the validator does not recognizes it.

`See also` complains of duplication if left as in the commit in the docstring. Not showed above.  