The TSNE class in the hana_ml.algorithms.pal.tsne module is used for T-distributed Stochastic Neighbour Embedding, with various parameters to control the algorithm's behavior, such as the number of iterations, learning rate, thread ratio, and more.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsne import TSNE
from hana_ml import DataFrame

# Assuming that connection_context is the connection to the HANA system
df_train = DataFrame(connection_context, 'TRAIN_DATA')  # Assuming 'TRAIN_DATA' is the table in HANA system

# Create a TSNE instance
tsne = TSNE(n_iter=500, n_components=3, angle=0, object_frequency=50, random_state=30)

# Perform fit_predict() on given dataframe
res, stats, obj = tsne.fit_predict(data=df_train, key='ID', perplexity=1.0)

# Collect the results
print(res.collect())
print(stats.collect())
print(obj.collect())
```

Please replace `'TRAIN_DATA'` with your actual table name in the HANA system. Also, ensure that `connection_context` is the connection to the HANA system.