class_docstr: 3D Swin Transformer Block.
  
  Args:
      dim (`int`):
          Number of input channels.
      num_heads (`int`):
          Number of attention heads.
      window_size (`tuple[int]`):
          Window size.
      shift_size (`tuple[int]`):
          Shift size for SW-MSA.
      mlp_ratio (`float`):
          Ratio of mlp hidden dim to embedding dim.
      qkv_bias (`bool`):
          If True, add a learnable bias to query, key, value. Default: True
      qk_scale (`float`):
          Override default qk scale of head_dim ** -0.5 if set.
      drop (`float`):
          Dropout rate. Default: 0.0
      attn_drop (`float`):
          Attention dropout rate. Default: 0.0
      drop_path(`float`):
          Stochastic depth rate. Default: 0.0
      act_layer (`nn.Module`):
          Activation layer. Default: nn.GELU
      norm_layer (`nn.Module`):
          Normalization layer.  Default: nn.LayerNorm
code: def forward_part2(self, x):
      return self.drop_path(self.mlp(self.norm2(x)))
  
file_path: towhee/towhee/models/layers/swin_transformer_block3d.py
imports: import torch
  from torch import nn
  from torch.utils import checkpoint
  import torch.nn.functional
  from towhee.models.layers.window_attention3d import WindowAttention3D
  from towhee.models.utils.window_partition3d import window_partition
  from towhee.models.utils.window_reverse3d import window_reverse
  from towhee.models.layers.mlp import Mlp
  from towhee.models.utils.get_window_size import get_window_size
  from towhee.models.layers.droppath import DropPath
label_desc: Use image node_id 3 for calling the SwinTransformerBlock3D obj's underlying member method code with example usage: obj.forward_part2(x) and returns: self