code: def forward(self, x, return_projection=True):
      region_latents = self.get_embedding(x)
      global_latent = reduce(region_latents, "b c h w -> b c", "mean")
  
      if not return_projection:
          return global_latent, region_latents
  
      view_projector = self._get_view_projector(global_latent)
      region_projector = self._get_region_projector(region_latents)
  
      region_latents = rearrange(region_latents, "b c h w -> b (h w) c")
  
      return (
          view_projector(global_latent),
          region_projector(region_latents),
          region_latents,
      )
  
imports: import copy
  import random
  from functools import wraps, partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torchvision import transforms
  from einops import rearrange, reduce, repeat
label_desc: Use image node_id 8 for calling the NetWrapper obj's underlying member method code with example usage: obj.forward(x, return_projection) and returns: view_projector,  region_projector,  region_latents, global_latent,  region_latents