code: def __init__(
      self,
      dim,
      num_heads,
      mlp_ratio=4.0,
      qkv_bias=False,
      qk_scale=None,
      drop=0.0,
      attn_drop=0.0,
      drop_path=0.0,
      init_values=None,
      act_layer=nn.GELU,
      norm_layer=nn.LayerNorm,
      window_size=None,
      attn_head_dim=None,
  ):
      super().__init__()
      self.norm1 = norm_layer(dim)
      self.attn = Attention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          qk_scale=qk_scale,
          attn_drop=attn_drop,
          proj_drop=drop,
          window_size=window_size,
          attn_head_dim=attn_head_dim,
      )
      # 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()
      )
      self.norm2 = norm_layer(dim)
      mlp_hidden_dim = int(dim * mlp_ratio)
      self.mlp = Mlp(
          in_features=dim,
          hidden_features=mlp_hidden_dim,
          act_layer=act_layer,
          drop=drop,
      )
  
      if init_values is not None and init_values > 0:
          self.gamma_1 = nn.Parameter(
              init_values * torch.ones((dim)), requires_grad=True
          )
          self.gamma_2 = nn.Parameter(
              init_values * torch.ones((dim)), requires_grad=True
          )
      else:
          self.gamma_1, self.gamma_2 = None, None
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from transformers import CLIPImageProcessor, CLIPVisionConfig
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, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, drop_path, init_values, act_layer, norm_layer, window_size, attn_head_dim)