code: def __init__(
      self,
      dim,
      key_dim,
      num_heads,
      mlp_ratio=4.0,
      attn_ratio=2.0,
      drop=0.0,
      drop_path=0.0,
      act_layer=nn.ReLU,
  ):
      super().__init__()
  
      self.dim = dim
      self.num_heads = num_heads
      self.mlp_ratio = mlp_ratio
  
      self.attn = SeaAttention(
          dim,
          key_dim=key_dim,
          num_heads=num_heads,
          attn_ratio=attn_ratio,
          activation=act_layer,
      )
  
      # 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 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,
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.models.backbones.transformer_utils import DropPath, zeros_, ones_
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(dim, key_dim, num_heads, mlp_ratio, attn_ratio, drop, drop_path, act_layer)