code: def forward(self, input, t):
      timestep_embed = expand_to_planes(
          self.timestep_embed(t[:, None]), input.shape
      )
      v = self.net(paddle.concat([input, timestep_embed], axis=1))
      alphas, sigmas = map(
          partial(append_dims, n=v.ndim), t_to_alpha_sigma(t)
      )
      pred = input * alphas - v * sigmas
      eps = input * sigmas + v * alphas
      return DiffusionOutput(v, pred, eps)
  
imports: import math
  from dataclasses import dataclass
  from functools import partial
  import paddle
  import paddle.nn
label_desc: Use image node_id 2 for calling the SecondaryDiffusionImageNet2 obj's underlying member method code with example usage: obj.forward(input, t) and returns: DiffusionOutput