The PipelineProgressStatusMonitor class in the hana_ml.visualizers.automl_progress module allows monitoring the progress of AutoML execution in different runtime environments, such as Jupyter, SAP Business Application Studio, VSCode, and console, by fetching the pipeline progress table at specified intervals.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.dataframe import ConnectionContext
from hana_ml.algorithms.pal.auto_ml import AutomaticClassification
from hana_ml.visualizers.automl_progress import PipelineProgressStatusMonitor
import uuid

# Define connection parameters
url = 'your_hana_url'
port = 'your_hana_port'
user = 'your_hana_user'
pwd = 'your_hana_password'

# Create a connection context
cc = ConnectionContext(url, port, user, pwd)

# Create an AutomaticClassification instance
progress_id = "automl_{}".format(uuid.uuid1())
auto_c = AutomaticClassification(generations=2,
                                 population_size=5,
                                 offspring_size=5,
                                 progress_indicator_id=progress_id)
auto_c.enable_workload_class("MY_WORKLOAD")

# Invoke a PipelineProgressStatusMonitor
progress_status_monitor = PipelineProgressStatusMonitor(connection_context=cc,
                                                        automatic_obj=auto_c)
progress_status_monitor.start()

# Fit the model
# Note: You need to define df_train before fitting the model
# auto_c.fit(data=df_train)
```

Please replace `'your_hana_url'`, `'your_hana_port'`, `'your_hana_user'`, and `'your_hana_password'` with your actual SAP HANA connection parameters. Also, you need to define `df_train` before fitting the model.