def forward(self, actions, batch_info):
while len(self.processes) < actions.shape[<NUM_LIT:0>]:<EOL><INDENT>len_action_space = self.action_space.shape[-<NUM_LIT:1>]<EOL>self.processes.append(<EOL>OrnsteinUhlenbeckNoiseProcess(<EOL>np.zeros(len_action_space), float(self.std_dev) * np.ones(len_action_space)<EOL>)<EOL>)<EOL><DEDENT>noise = torch.from_numpy(np.stack([x() for x in self.processes])).float().to(actions.device)<EOL>return torch.min(torch.max(actions + noise, self.low_tensor), self.high_tensor)<EOL>
Return model step after applying noise