class_docstr: Fully Connected layer with on the fly adaptive mask.
  If needed, a score matrix is created to store the importance of each associated weight.
code: def init_mask(self):
      if self.mask_init == "constant":
          init.constant_(self.mask_scores, val=self.mask_scale)
      elif self.mask_init == "uniform":
          init.uniform_(
              self.mask_scores, a=-self.mask_scale, b=self.mask_scale
          )
      elif self.mask_init == "kaiming":
          init.kaiming_uniform_(self.mask_scores, a=math.sqrt(5))
  
file_path: transformers/examples/research_projects/movement-pruning/emmental/modules/masked_nn.py
imports: import math
  import torch
  from torch import nn
  from torch.nn import init
  from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
label_desc: Use image node_id 2 for calling the MaskedLinear obj's underlying member method code with example usage: obj.init_mask() without return types