### Apache Airflow Provider(s)

amazon

### Versions of Apache Airflow Providers

_No response_

### Apache Airflow version

2.3.3

### Operating System

MacOs/Linux

### Deployment

Docker-Compose

### Deployment details

_No response_

### What happened

sql_to_s3.py transfer has top level python code imports considered as Bad Practices:

https://github.com/apache/airflow/blob/be31214dcf14db39b7a5f422ca272cdc13e08268/airflow/providers/amazon/aws/transfers/sql_to_s3.py#L26

According to the [official docs](https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code):

```python
import numpy as np  # <-- THIS IS A VERY BAD IDEA! DON'T DO THAT!
```

All imports that are not related to DAG structure and creation should be moved to callable functions, such as the `execute` method.

This causes timeout errors while filling the `DagBag`:

```
  File "/opt/airflow/dags/mydag.py", line 6, in <module>
    from airflow.providers.amazon.aws.transfers.sql_to_s3 import SqlToS3Operator
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/transfers/sql_to_s3.py", line 25, in <module>
    import pandas as pd
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/__init__.py", line 50, in <module>
    from pandas.core.api import (
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/api.py", line 48, in <module>
    from pandas.core.groupby import (
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/__init__.py", line 1, in <module>
    from pandas.core.groupby.generic import (
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 73, in <module>
    from pandas.core.frame import DataFrame
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/frame.py", line 193, in <module>
    from pandas.core.series import Series
  File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/series.py", line 141, in <module>
    import pandas.plotting
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 724, in exec_module
  File "<frozen importlib._bootstrap_external>", line 859, in get_code
  File "<frozen importlib._bootstrap_external>", line 917, in get_data
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/timeout.py", line 68, in handle_timeout
    raise AirflowTaskTimeout(self.error_message)
airflow.exceptions.AirflowTaskTimeout: DagBag import timeout for /opt/airflow/dags/mydag.py after 30.0s.
Please take a look at these docs to improve your DAG import time:
* https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code
* https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#reducing-dag-complexity, PID: 7
```

### What you think should happen instead

_No response_

### How to reproduce

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self.parameters = parameters
        if "path_or_buf" in self.pd_kwargs:
            raise AirflowException("The argument path_or_buf is not allowed, please remove it")
        try:
            self.file_format = FILE_FORMAT[file_format.upper()]
        except KeyError:
            raise AirflowException(f"The argument file_format doesn't support {file_format} value.")
    @staticmethod
    def _fix_dtypes(df: pd.DataFrame, file_format: FILE_FORMAT) -> None:
        """
        Mutate DataFrame to set dtypes for float columns containing NaN values.
        Set dtype of object to str to allow for downstream transformations.
        """
        for col in df:
            if df[col].dtype.name == "object" and file_format == "parquet":
                
                
                df[col] = df[col].astype(str)
            if "float" in df[col].dtype.name and df[col].hasnans:
                
                notna_series = df[col].dropna().values
                if np.equal(notna_series, notna_series.astype(int)).all():
                    
                    df[col] = np.where(df[col].isnull(), None, df[col])
                    df[col] = df[col].astype(pd.Int64Dtype())
                elif np.isclose(notna_series, notna_series.astype(int)).all():
                    
                    df[col] = np.where(df[col].isnull(), None, df[col])
                    df[col] = df[col].astype(pd.Float64Dtype())
    def execute(self, context: Context) -> None: