code: def _create_feature_mask(self):
      feature_masks = torch.cat(
          [
              torch.distributions.Beta(
                  torch.tensor([random.uniform(0.5, 10.0)]),
                  torch.tensor([random.uniform(0.5, 10.0)]),
              )
              .sample((self.n_features_in,))
              .squeeze(-1)
              for _ in range(self.n_stages)
          ]
      ).reshape(self.n_stages, self.n_features_in)
      return nn.Parameter(
          feature_masks,
          requires_grad=True,
      )
  
imports: import random
  from typing import Callable
  import torch
  import torch.nn
  from pytorch_tabular.models.common.layers.activations import RSoftmax, entmax15
label_desc: Use image node_id 2 for calling the GatedFeatureLearningUnit obj's underlying member method code with example usage: obj._create_feature_mask() and returns: nn