code: def forward(self, x, temb=None):
      h = self.act(self.GroupNorm_0(x))
  
      if self.up:
          if self.fir:
              h = up_or_down_sampling.upsample_2d(
                  h, self.fir_kernel, factor=2
              )
              x = up_or_down_sampling.upsample_2d(
                  x, self.fir_kernel, factor=2
              )
          else:
              h = up_or_down_sampling.naive_upsample_2d(h, factor=2)
              x = up_or_down_sampling.naive_upsample_2d(x, factor=2)
      elif self.down:
          if self.fir:
              h = up_or_down_sampling.downsample_2d(
                  h, self.fir_kernel, factor=2
              )
              x = up_or_down_sampling.downsample_2d(
                  x, self.fir_kernel, factor=2
              )
          else:
              h = up_or_down_sampling.naive_downsample_2d(h, factor=2)
              x = up_or_down_sampling.naive_downsample_2d(x, factor=2)
  
      h = self.Conv_0(h)
      # Add bias to each feature map conditioned on the time embedding
      if temb is not None:
          h += self.Dense_0(self.act(temb))[:, :, None, None]
      h = self.act(self.GroupNorm_1(h))
      h = self.Dropout_0(h)
      h = self.Conv_1(h)
  
      if self.in_ch != self.out_ch or self.up or self.down:
          x = self.Conv_2(x)
  
      if not self.skip_rescale:
          return x + h
      else:
          return (x + h) / np.sqrt(2.0)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.ncsnpp_utils import layers, up_or_down_sampling
label_desc: Use image node_id 2 for calling the ResnetBlockBigGANpp obj's underlying member method code with example usage: obj.forward(x, temb) and returns: unknown, unknown