You have the following code segment: def loading_pattern_sample(df_source): try: deltatable = DeltaTable.forName(spark, target_table) except Exception as e: df_source.write.format('delta').mode('overw

You have the following code segment: def loading_pattern_sample(df_source): try: deltatable = DeltaTable.forName(spark, target_table) except Exception as e: df_source.write.format('delta').mode('overwrite').saveAsTable(f'{target_table}') except Exception as e: print(f'load for table {target_table} failed with error: {str(e)}') return try: change_detection_columns = [col for col in df_source.columns if col not in candidate_key] match_condition = ' AND '.join([f'target.{col} = source.{col}' for col in candidate_key]) update_condition = ' OR '.join([f'target.{col} != source.{col}' for col in change_detection_columns]) update_expr = {col: f'source.{col}' for col in df_source.columns} merge_operation = deltatable.alias('target').merge( source=df_source.alias('source'), condition=match_condition ).whenMatchedUpdate( condition=update_condition, set=update_expr ).whenNotMatchedInsertAll() merge_operation.execute() except Exception as e: print(f'insert operation for table {target_table} failed with error: {str(e)}') return Based on the code, does the loading pattern support both full and incremental loading requirements?