Create DATETIMECONVERTWINDOWHOP function

# Create a Window HOP function similar to DATETIMECONVERT.

The `DATETIMECONVERTWINDOWHOP` converts the value from a column that contains an epoch timestamp into another time unit and buckets based on the given time granularity and window hop size.

A hopping time window has a fixed duration (hopWindow paramenter) and hops by a specified hop interval (outputGranularity parameter). If the hop interval is smaller than the window size, hopping windows are overlapping. Thus, rows can be assigned to multiple windows. For example, a hopping window of 15 minutes size and 5 minute hop interval assigns each row to 3 different windows of 15 minute size, which are evaluated in an interval of 5 minutes. This is why the function returns an array of timestamps, in this example the array would contain 3 elements.

# Signature
```
DATETIMECONVERTWINDOWHOP(columnName, inputFormat, outputFormat, outputGranularity, hopWindow)
inputFormat and outputFormat are defined using the following structure:
:::
```

# Usage Examples
These examples are based on the [Batch JSON Quick Start](https://docs.pinot.apache.org/basics/getting-started/quick-start#batch-json).
created_at_timestamp from milliseconds since epoch to seconds since epoch, bucketed to 1 hour window with 15 min granularity:

```
select id,
created_at_timestamp,
cast(created_at_timestamp AS long) AS timeInMs,
DATETIMECONVERTWINDOWHOP(
created_at_timestamp,
'1:MILLISECONDS:EPOCH',
'1:SECONDS:EPOCH',
'15:MINUTES',
'1:HOURS'
) AS windowHops
from githubEvents
WHERE id = 7044874134
```
id | created_at_timestamp | timeInMs | windowHops
-- | -- | -- | --
7044874134 | 2018-01-01 11:00:00.0 | 1514804402000 | [1514804402,   1514803502, 1514802602, 1514801702]

Moving window of unique user counts per hour with 15 min granularity:

```
select
DATETIMECONVERTWINDOWHOP(
created_at_timestamp,
'1:MILLISECONDS:EPOCH',
'1:SECONDS:EPOCH',
'15:MINUTES',
'1:HOURS'
) AS windowHops,
DISTINCTCOUNT(id) AS unique_prs
from githubEvents
group by 1
ORDER BY 1
```

Start by locating the existing DATETIMECONVERT function implementation and its tests, then trace how SQL functions are registered and validated. Add the analogous hopping-window behavior described by the signature and examples, and verify the documented query results and returned timestamp arrays with focused tests.

Written by the indexing model from the issue text.