code: def __init__(
      self,
      ape=False,
      attn_drop_rate=0.0,
      depths=(2, 2, 6, 2),
      drop_path_rate=0.5,
      drop_rate=0.0,
      embed_dim=96,
      mlp_ratio=4.0,
      num_heads=(3, 6, 12, 24),
      layers=(0, 1, 2, 3),
      patch_norm=True,
      qk_scale=None,
      qkv_bias=True,
      use_checkpoint=False,
      window_size=7,
      activation=ACT_GELU,
      pretrained="https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth",
  ):
      super(SwinT, self).__init__(
          ape=ape,
          attn_drop_rate=attn_drop_rate,
          depths=depths,
          drop_path_rate=drop_path_rate,
          drop_rate=drop_rate,
          embed_dim=embed_dim,
          mlp_ratio=mlp_ratio,
          num_heads=num_heads,
          layers=layers,
          patch_norm=patch_norm,
          qk_scale=qk_scale,
          qkv_bias=qkv_bias,
          use_checkpoint=use_checkpoint,
          window_size=window_size,
          activation=activation,
          pretrained=pretrained,
      )
  
imports: from collections import OrderedDict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import Tensor
  from .common import EncoderModule, _take, make_n_channel_input
  from ..activations import get_activation_block, ACT_GELU
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
label_desc: Use image node_id 1 to create a new SwinT object from inherited base classes: SwinTransformer with example: obj = SwinT(ape, attn_drop_rate, depths, drop_path_rate, drop_rate, embed_dim, mlp_ratio, num_heads, layers, patch_norm, qk_scale, qkv_bias, use_checkpoint, window_size, activation, pretrained)