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__(
          input_size,
          hidden_size,
          num_layers,
          bias,
          bidirectional,
          union,
          with_fc,
      )
      if self.with_vertical:
          self.rnn_v = nn.LSTM(
              input_size,
              hidden_size,
              num_layers,
              batch_first=True,
              bias=bias,
              bidirectional=bidirectional,
          )
      if self.with_horizontal:
          self.rnn_h = nn.LSTM(
              input_size,
              hidden_size,
              num_layers,
              batch_first=True,
              bias=bias,
              bidirectional=bidirectional,
          )
  
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 LSTM2d object from inherited base classes: RNN2dBase with example: obj = LSTM2d(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)