Move duckdb & pandas import in tutorial DAG into task (#35964)

This improves the code as per best practices and avoids import
error if duckdb is not installed

(cherry picked from commit f0ba2dc)

airflow/example_dags/tutorial_objectstorage.py

@@ -47,7 +47,6 @@
 # [END create_object_storage_path]
 
 
-# [START instantiate_dag]
 @dag(
     schedule=None,
     start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
@@ -62,9 +61,6 @@ def tutorial_objectstorage():
     located
     [here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/objectstorage.html)
     """
-    # [END instantiate_dag]
-    import duckdb
-    import pandas as pd
 
     # [START get_air_quality_data]
     @task
@@ -74,6 +70,8 @@ def get_air_quality_data(**kwargs) -> ObjectStoragePath:
         This task gets air quality data from the Finnish Meteorological Institute's
         open data API. The data is saved as parquet.
         """
+        import pandas as pd
+
         execution_date = kwargs["logical_date"]
         start_time = kwargs["data_interval_start"]
 
@@ -113,6 +111,8 @@ def analyze(path: ObjectStoragePath, **kwargs):
         #### Analyze
         This task analyzes the air quality data, prints the results
         """
+        import duckdb
+
         conn = duckdb.connect(database=":memory:")
         conn.register_filesystem(path.fs)
         conn.execute(f"CREATE OR REPLACE TABLE airquality_urban AS SELECT * FROM read_parquet('{path}')")