code: def forward(self, inputs, time, cond):
      x = self.input_projection(inputs)
      x = F.leaky_relu(x, 0.4)
  
      diffusion_step = self.diffusion_embedding(time)
      cond_up = self.cond_upsampler(cond)
      skip = []
      for layer in self.residual_layers:
          x, skip_connection = layer(x, cond_up, diffusion_step)
          skip.append(skip_connection)
  
      x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(
          len(self.residual_layers)
      )
      x = self.skip_projection(x)
      x = F.leaky_relu(x, 0.4)
      x = self.output_projection(x)
      return x
  
imports: import math
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the EpsilonTheta obj's underlying member method code with example usage: obj.forward(inputs, time, cond) and returns: x