The Forecast TCN model deployment through the UI does not work.

### Operating System

Windows

### Version Information

Recently we have discovered a problem due to the error in the DNN scoring script file. **Please see the workaround in Additional information section.**
The error was related to the scorings script lines:

```python
result_sample = StandardPythonParameterType({
'forecast': NumpyParameterType(0.0),
'index': PandasParameterType(pd.DataFrame({}), enforce_shape=False)
})
```

The deployment results in a stack trace similar to the one provided below:

```
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 74, in load_script
main_module_spec.loader.exec_module(user_module)
File "", line 843, in exec_module
File "", line 219, in _call_with_frames_removed
File "/var/azureml-app/outputs/scoring_file_v_2_0_0.py", line 27, in
'forecast': NumpyParameterType(0.0),
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/inference_schema/parameter_types/numpy_parameter_type.py", line 33, in __init__
raise Exception("Invalid sample input provided, must provide a sample Numpy array.")
Exception: Invalid sample input provided, must provide a sample Numpy array.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/aml_blueprint.py", line 88, in setup
self.user_script.load_script(config.app_root)
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 76, in load_script
raise UserScriptImportException(ex) from ex
```

The error was already fixed, and the issue is expected to be resolved two weeks after the release of `azureml-sdk` v. 1.50.0.

### Steps to reproduce

1. In the AzureML workspace UI in the Forecast TCN run in Models section, select one of models and click Deploy.
2. Select Managed online endpoint.
3. Answer the necessary questions.
4. Wait for deployment.

### Expected behavior

The functional model should be deployed.

### Actual behavior

The deployment will fail and the deployment log will contain a stack trace:

```
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 74, in load_script
main_module_spec.loader.exec_module(user_module)
File "", line 843, in exec_module
File "", line 219, in _call_with_frames_removed
File "/var/azureml-app/outputs/scoring_file_v_2_0_0.py", line 27, in
'forecast': NumpyParameterType(0.0),
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/inference_schema/parameter_types/numpy_parameter_type.py", line 33, in __init__
raise Exception("Invalid sample input provided, must provide a sample Numpy array.")
Exception: Invalid sample input provided, must provide a sample Numpy array.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/aml_blueprint.py", line 88, in setup
self.user_script.load_script(config.app_root)
File "/azureml-envs/azureml-automl-dnn-forecasting-gpu/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 76, in load_script
raise UserScriptImportException(ex) from ex
```

### Addition information

**Workaround**
1. Download the model of interest from the list of ForecastTCN models. The downloaded archive will contain three files: `conda_env_v_1_0_0.yml`, `model.pt` or model.pt and `scoring_file_v_2_0_0.py`.
2. Register the `model.pt` from UI: Register>From Local files.
3. Select "Unspecified type" and select the model on the local file system, where the files were extraced. **Do not select the archive itself.**
4. Name the model and register it.
5. Edit the conda_env_v_1_0_0.yml file and add the azureml-defaults package of the same version as other azureml package, if it is present, leave the file as is.
6. At the environment tab create the environment using conda file:
- Select Start from existing environment
- Select Container registry image
- Next to the “docker pull” set the image as follows `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest`
- Upload the conda file
7. Wait until the environment is built. You can monitor it by clicking "Build logs".
8. Return to models tab.
9. Select new model and click "Deploy" > Real time endpoint.
10. Correct the `scoring_file_v_2_0_0.py` so that instead of

```python
result_sample = StandardPythonParameterType({
'forecast': NumpyParameterType(0.0),
'index': PandasParameterType(pd.DataFrame({}), enforce_shape=False)
})
```
It will contain:
```python
result_sample = StandardPythonParameterType({
'forecast': NumpyParameterType(np.array([0])),
'index': PandasParameterType(pd.DataFrame({}), enforce_shape=False)
})
```
11. Select the built image we have build in section (7) and scoring script `scoring_file_v_2_0_0.py` and perform the deployment.