code: def forward(self, x):
      for linear in self.layers:
          x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
      return x
  
imports: from math import sqrt
  import torch
  import torch.distributions
  from torch.autograd import Variable
  from torch import nn
  from torch.nn import functional
  from .layers import ConvNorm, LinearNorm, GlobalAvgPool
  from .utils import to_gpu, get_mask_from_lengths
label_desc: Use image node_id 2 for calling the Prenet obj's underlying member method code with example usage: obj.forward(x) and returns: x