class_docstr: Parallel block class. 
class_docstr_tok: ["Parallel","block","class","."]
code: def forward(self, x1, x2, x3, x4, sizes: List[Tuple[int, int]]):
      _, S2, S3, S4 = sizes
      cur2 = self.norm12(x2)
      cur3 = self.norm13(x3)
      cur4 = self.norm14(x4)
      cur2 = self.factoratt_crpe2(cur2, size=S2)
      cur3 = self.factoratt_crpe3(cur3, size=S3)
      cur4 = self.factoratt_crpe4(cur4, size=S4)
      upsample3_2 = self.upsample(cur3, factor=2.0, size=S3)
      upsample4_3 = self.upsample(cur4, factor=2.0, size=S4)
      upsample4_2 = self.upsample(cur4, factor=4.0, size=S4)
      downsample2_3 = self.downsample(cur2, factor=2.0, size=S2)
      downsample3_4 = self.downsample(cur3, factor=2.0, size=S3)
      downsample2_4 = self.downsample(cur2, factor=4.0, size=S2)
      cur2 = cur2 + upsample3_2 + upsample4_2
      cur3 = cur3 + upsample4_3 + downsample2_3
      cur4 = cur4 + downsample3_4 + downsample2_4
      x2 = x2 + self.drop_path(cur2)
      x3 = x3 + self.drop_path(cur3)
      x4 = x4 + self.drop_path(cur4)
  
      # MLP.
      cur2 = self.norm22(x2)
      cur3 = self.norm23(x3)
      cur4 = self.norm24(x4)
      cur2 = self.mlp2(cur2)
      cur3 = self.mlp3(cur3)
      cur4 = self.mlp4(cur4)
      x2 = x2 + self.drop_path(cur2)
      x3 = x3 + self.drop_path(cur3)
      x4 = x4 + self.drop_path(cur4)
  
      return x1, x2, x3, x4
  
imports: from functools import partial
  from typing import Tuple, List, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 5 for calling the ParallelBlock obj's underlying member method code with example usage: obj.forward(x1, x2, x3, x4, sizes) and returns: x1,  x2,  x3,  x4