code: def __init__(
      self,
      dim: int,
      num_heads: int,
      num_patches: int,
      mlp_ratio: float = 4.0,
      qkv_bias: bool = False,
      qk_scale: Optional[None] = None,
      drop: float = 0.0,
      attn_drop: float = 0.0,
      drop_path: float = 0.0,
      act_layer: Callable = nn.ReLU6,
      norm_layer: str = "ln",
      patch_n: int = 144,
  ):
      super().__init__()
  
      if norm_layer == "bn":
          self.norm1 = VITBatchNorm(num_features=num_patches)
          self.norm2 = VITBatchNorm(num_features=num_patches)
      elif norm_layer == "ln":
          self.norm1 = nn.LayerNorm(dim)
          self.norm2 = nn.LayerNorm(dim)
  
      self.attn = Attention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          qk_scale=qk_scale,
          attn_drop=attn_drop,
          proj_drop=drop,
      )
      # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
      mlp_hidden_dim = int(dim * mlp_ratio)
      self.mlp = Mlp(
          in_features=dim,
          hidden_features=mlp_hidden_dim,
          act_layer=act_layer,
          drop=drop,
      )
      self.extra_gflops = (
          num_heads * patch_n * (dim // num_heads) * patch_n * 2
      ) / (1000**3)
  
imports: import torch
  import torch.nn
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from typing import Optional, Callable
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(dim, num_heads, num_patches, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, drop_path, act_layer, norm_layer, patch_n)