code: def __init__(
      self,
      dim,
      num_heads,
      mlp_ratio,
      qkv_bias: bool,
      qk_scale: Optional[float],
      drop: float,
      attn_drop: float,
      drop_path: float,
      activation: str,
      sr_ratio: int,
      norm_layer=nn.LayerNorm,
  ):
      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,
          sr_ratio=sr_ratio,
      )
      # 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,
          activation=activation,
          drop=drop,
      )
  
      self.apply(self._init_weights)
  
imports: import math
  from functools import partial
  from typing import Optional
  import torch
  import torch.nn
  from pytorch_toolbelt.modules.interfaces import AbstractEncoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.activations import instantiate_activation_block, ACT_GELU
  from pytorch_toolbelt.modules.drop_path import DropPath
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, activation, sr_ratio, norm_layer)