code: def __init__(
      self,
      input_size: int,
      hidden_size: int,
      num_layers: int = 1,
      bias: bool = True,
      bidirectional: bool = True,
      union="cat",
      with_fc=True,
  ):
      super().__init__()
  
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.output_size = (
          2 * hidden_size if bidirectional else hidden_size
      )
      self.union = union
  
      self.with_vertical = True
      self.with_horizontal = True
      self.with_fc = with_fc
  
      self.fc = None
      if with_fc:
          if union == "cat":
              self.fc = nn.Linear(2 * self.output_size, input_size)
          elif union == "add":
              self.fc = nn.Linear(self.output_size, input_size)
          elif union == "vertical":
              self.fc = nn.Linear(self.output_size, input_size)
              self.with_horizontal = False
          elif union == "horizontal":
              self.fc = nn.Linear(self.output_size, input_size)
              self.with_vertical = False
          else:
              raise ValueError("Unrecognized union: " + union)
      elif union == "cat":
          pass
          if 2 * self.output_size != input_size:
              raise ValueError(
                  f"The output channel {2 * self.output_size} is different from the input channel {input_size}."
              )
      elif union == "add":
          pass
          if self.output_size != input_size:
              raise ValueError(
                  f"The output channel {self.output_size} is different from the input channel {input_size}."
              )
      elif union == "vertical":
          if self.output_size != input_size:
              raise ValueError(
                  f"The output channel {self.output_size} is different from the input channel {input_size}."
              )
          self.with_horizontal = False
      elif union == "horizontal":
          if self.output_size != input_size:
              raise ValueError(
                  f"The output channel {self.output_size} is different from the input channel {input_size}."
              )
          self.with_vertical = False
      else:
          raise ValueError("Unrecognized union: " + union)
  
      self.rnn_v = RNNIdentity()
      self.rnn_h = RNNIdentity()
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new RNN2dBase object from inherited base classes: nn with example: obj = RNN2dBase(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)