change **forecasting_parameters

passing forecasting_parameters as mentioned previously in docs is not valid and return exception of type (TypeError: type object argument after ** must be a mapping, not ForecastingParameters.

but on this tutorial, it's written correctly 
https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

Therefore, I'm updating how it's being passed

Author: mobaniha

articles/machine-learning/how-to-auto-train-forecast.md

@@ -126,7 +126,7 @@ automl_config = AutoMLConfig(task='forecasting',
                              cv_step_size = "auto", # Could be customized as an integer
                              enable_ensembling=False,
                              verbosity=logging.INFO,
-                             **forecasting_parameters)
+                             forecasting_parameters=forecasting_parameters)
 ```
 
 The amount of data required to successfully train a forecasting model with automated ML is influenced by the `forecast_horizon`, `n_cross_validations`, and `target_lags` or `target_rolling_window_size` values specified when you configure your `AutoMLConfig`. 
@@ -229,7 +229,7 @@ To enable deep learning, set the `enable_dnn=True` in the `AutoMLConfig` object.
 automl_config = AutoMLConfig(task='forecasting',
                              enable_dnn=True,
                              ...
-                             **forecasting_parameters)
+                             forecasting_parameters=forecasting_parameters)
 ```
 > [!Warning]
 > When you enable DNN for experiments created with the SDK, [best model explanations](how-to-machine-learning-interpretability-automl.md) are disabled.