code: def __init__(self, hid_dim, dropout_prob=0.2):
      super().__init__()
      self.linear_in = nn.Linear(hid_dim, 2 * hid_dim)
      self.activation = nn.ReLU()
      self.dropout = DeterministicDropout(dropout_prob)
      self.linear_out = nn.Linear(2 * hid_dim, hid_dim)
  
file_path: hivemind/hivemind/moe/server/layers/dropout.py
imports: import torch.autograd
  from torch import nn
  from hivemind.moe.server.layers.custom_experts import register_expert_class
label_desc: Use image node_id 1 to create a new DeterministicDropoutNetwork object from inherited base classes: nn with example: obj = DeterministicDropoutNetwork(hid_dim, dropout_prob)