code: def __init__(
      self,
      dim,
      num_heads,
      mlp_ratio=4.0,
      qkv_bias=False,
      qk_scale=None,
      drop=0.0,
      attn_drop=0.0,
      mlp_drop=0.0,
      post_mlp_drop=0.0,
      drop_path=0.0,
      act_layer=nn.GELU,
      norm_layer=nn.LayerNorm,
      layer_norm_first=True,
      ffn_targets=False,
      cosine_attention=False,
  ):
      super().__init__()
  
      self.layer_norm_first = layer_norm_first
      self.ffn_targets = ffn_targets
  
      from timm.models.vision_transformer import DropPath, Mlp
  
      self.norm1 = norm_layer(dim)
      self.attn = AltAttention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          qk_scale=qk_scale,
          attn_drop=attn_drop,
          proj_drop=drop,
          cosine_attention=cosine_attention,
      )
  
      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=mlp_drop,
      )
      self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from dataclasses import dataclass
  from fairseq.modules import LayerNorm, SamePad, SamePad2d, TransposeLast
label_desc: Use image node_id 1 to create a new AltBlock object from inherited base classes: nn with example: obj = AltBlock(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, mlp_drop, post_mlp_drop, drop_path, act_layer, norm_layer, layer_norm_first, ffn_targets, cosine_attention)