code: def forward(self, x):
      if self.lock_dim is None:
          return self.dropout(x)
      else:
          mask = self.dropout(
              pt.ones_like(x).narrow(self.lock_dim, 0, 1)
          ).expand_as(x)
          return mask * x
  
imports: from typing import List, Optional
  from collections import deque
  import math
  import torch
  from torch import Tensor
  from torch import nn
  from torch.distributions import Distribution
  from torch.distributions import Normal
label_desc: Use image node_id 2 for calling the LockedDropout obj's underlying member method code with example usage: obj.forward(x) and returns: self, unknown