class_docstr: Takes an initial labeling and uses a mixture of Gaussians to approximate each classes
  distribution in the feature space. Each unlabeled element is then assigned a probability
  of belonging to each class based on it's fit to each classes approximated distribution.
  
  See:
      https://en.wikipedia.org/wiki/Mixture_model
docstr: Applies the current model to a set of feature vectors.
  
  Args:
      features (torch.Tensor): feature vectors for each element.
  
  Returns:
      output (torch.Tensor): class assignment probabilities for each element.
code: def apply(self, features):
      """
      Applies the current model to a set of feature vectors.
  
      Args:
          features (torch.Tensor): feature vectors for each element.
  
      Returns:
          output (torch.Tensor): class assignment probabilities for each element.
      """
      return _ApplyFunc.apply(
          self.params, features, self.compiled_extension
      )
  
file_path: MONAI/monai/networks/layers/gmm.py
imports: from __future__ import annotations
  import torch
  from monai._extensions.loader import load_module
label_desc: Use image node_id 4 for calling the GaussianMixtureModel obj's underlying member method code with example usage: obj.apply(features) and returns: _ApplyFunc