code: def __init__(
      self,
      dim: int,
      input_resolution: int | tuple[int, int],
      num_heads: int,
      window_size: int = 7,
      mlp_ratio: float = 4.0,
      drop: float = 0.0,
      drop_path: float = 0.0,
      local_conv_size: int = 3,
      activation: type[Module] = nn.GELU,
  ) -> None:
      KORNIA_CHECK(
          dim % num_heads == 0, "dim must be divislbe by num_heads"
      )
      super().__init__()
      self.input_resolution = _make_pair(input_resolution)
      self.window_size = window_size
      head_dim = dim // num_heads
  
      self.attn = Attention(
          dim, head_dim, num_heads, 1.0, (window_size, window_size)
      )
      self.drop_path1 = DropPath(drop_path)
      self.local_conv = ConvBN(
          dim, dim, local_conv_size, 1, local_conv_size // 2, dim
      )
      self.mlp = MLP(dim, int(dim * mlp_ratio), dim, activation, drop)
      self.drop_path2 = DropPath(drop_path)
  
imports: from __future__ import annotations
  import itertools
  import warnings
  from typing import Any, Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils import checkpoint
  from kornia.contrib.models.common import DropPath, LayerNorm2d, window_partition, window_unpartition
  from kornia.core import Module, Tensor
  from kornia.core.check import KORNIA_CHECK
label_desc: Use image node_id 1 to create a new TinyViTBlock object from inherited base classes: Module with example: obj = TinyViTBlock(dim, input_resolution, num_heads, window_size, mlp_ratio, drop, drop_path, local_conv_size, activation)