docstr: SwinTransformer and Conv Block
          
docstr_tok: ["SwinTransformer","and","Conv","Block"]
code: def __init__(
      self,
      conv_dim,
      trans_dim,
      head_dim,
      window_size,
      drop_path,
      type="W",
      input_resolution=None,
  ):
      """SwinTransformer and Conv Block"""
      super(ConvTransBlock, self).__init__()
      self.conv_dim = conv_dim
      self.trans_dim = trans_dim
      self.head_dim = head_dim
      self.window_size = window_size
      self.drop_path = drop_path
      self.type = type
      self.input_resolution = input_resolution
  
      assert self.type in ["W", "SW"]
      if self.input_resolution <= self.window_size:
          self.type = "W"
  
      self.trans_block = Block(
          self.trans_dim,
          self.trans_dim,
          self.head_dim,
          self.window_size,
          self.drop_path,
          self.type,
          self.input_resolution,
      )
      self.conv1_1 = nn.Conv2d(
          self.conv_dim + self.trans_dim,
          self.conv_dim + self.trans_dim,
          1,
          1,
          0,
          bias=True,
      )
      self.conv1_2 = nn.Conv2d(
          self.conv_dim + self.trans_dim,
          self.conv_dim + self.trans_dim,
          1,
          1,
          0,
          bias=True,
      )
  
      self.conv_block = nn.Sequential(
          nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
          nn.ReLU(True),
          nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
      )
  
imports: import numpy
  import torch
  import torch.nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
  from timm.models.layers import trunc_normal_, DropPath
label_desc: Use image node_id 1 to create a new ConvTransBlock object from inherited base classes: nn with example: obj = ConvTransBlock(conv_dim, trans_dim, head_dim, window_size, drop_path, type, input_resolution)