pycave kmeans-gpu disable model trainer outputs from being printed
This is a code snippet to run KMeans using GPU.
Documentation-link:https://pycave.borchero.com/sites/generated/clustering/kmeans/pycave.clustering.KMeans.html
import torch
from pycave.clustering import KMeans

X = torch.cat([
    torch.randn(1000, 6) - 5,
    torch.randn(1000, 6),
    torch.randn(1000, 6) + 5,
])

estimator = KMeans(num_clusters = 3, trainer_params=dict(gpus=1, 
                                                         enable_progress_bar=0, 
                                                         max_epochs=100,))
labels = estimator.fit_predict(X).numpy()

pd.value_counts(labels)

The issue is with how to disable the console output from the estimator.
Current Output:
Running initialization...

{'batch_size': 3000, 'collate_fn': &lt;function collate_tensor at 0x000002BE21221700&gt;}

Fitting K-Means...

{'batch_size': 3000, 'collate_fn': &lt;function collate_tensor at 0x000002BE21221700&gt;}
{'batch_size': 1, 'sampler': None, 'batch_sampler': &lt;pytorch_lightning.overrides.distributed.IndexBatchSamplerWrapper object at 0x000002BE593A55B0&gt;, 'collate_fn': &lt;function collate_tensor at 0x000002BE21221700&gt;, 'shuffle': False, 'drop_last': False}

0    1000
2    1000
1    1000
dtype: int64

Expected Output:
0    1000
2    1000
1    1000
dtype: int64

Info regarding trainer_params parameter
(Optional[Dict[str, Any]]) --
Initialization parameters to use when initializing a PyTorch Lightning trainer. By default, it disables various stdout logs unless PyCave is configured to do verbose logging. Checkpointing and logging are disabled regardless of the log level.

The dictionaries that are printed should never be there, that's a bug in a dependency. Resolved in the latest build.
As far as the PyCave logs are concerned (Running initialization... and Fitting K-Means...), you can turn them off easily by adding the following:
import logging
from pycave import set_logging_level

set_logging_level(logging.WARNING)

Note that set_logging_level(logging.WARNING) also turns off the progress bar and the model summary automatically so you don't have to set these flags explicitly.
