code: def __init__(
      self,
      dim: int,
      input_resolution: int | tuple[int, int],
      depth: int,
      num_heads: int,
      window_size: int,
      mlp_ratio: float = 4.0,
      drop: float = 0.0,
      drop_path: float | list[float] = 0.0,
      downsample: Optional[Module] = None,
      use_checkpoint: bool = False,
      local_conv_size: int = 3,
      activation: type[Module] = nn.GELU,
  ) -> None:
      super().__init__()
      self.use_checkpoint = use_checkpoint
  
      self.blocks = nn.ModuleList(
          [
              TinyViTBlock(
                  dim,
                  input_resolution,
                  num_heads,
                  window_size,
                  mlp_ratio,
                  drop,
                  drop_path[i]
                  if isinstance(drop_path, list)
                  else drop_path,
                  local_conv_size,
                  activation,
              )
              for i in range(depth)
          ]
      )
  
      # patch merging layer
      self.downsample = downsample
  
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 BasicLayer object from inherited base classes: Module with example: obj = BasicLayer(dim, input_resolution, depth, num_heads, window_size, mlp_ratio, drop, drop_path, downsample, use_checkpoint, local_conv_size, activation)