code: def forward(self, x):
      h0 = Variable(
          torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size)
      )
      out, _ = self.gru(x, h0)
      if self.every_frame:
          out = self.fc(out)  # predictions based on every time step
      else:
          out = self.fc(
              out[:, -1, :]
          )  # predictions based on last time-step
      return out
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the GRU obj's underlying member method code with example usage: obj.forward(x) and returns: out