code: def __init__(
      self,
      dim,
      num_heads,
      mixer="Global",
      local_mixer=[7, 11],
      HW=None,
      mlp_ratio=4.0,
      qkv_bias=False,
      qk_scale=None,
      drop=0.0,
      attn_drop=0.0,
      drop_path=0.0,
      act_layer=nn.GELU,
      norm_layer="nn.LayerNorm",
      epsilon=1e-6,
      prenorm=True,
  ):
      super().__init__()
      if isinstance(norm_layer, str):
          self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
      else:
          self.norm1 = norm_layer(dim)
      if mixer == "Global" or mixer == "Local":
          self.mixer = Attention(
              dim,
              num_heads=num_heads,
              mixer=mixer,
              HW=HW,
              local_k=local_mixer,
              qkv_bias=qkv_bias,
              qk_scale=qk_scale,
              attn_drop=attn_drop,
              proj_drop=drop,
          )
      elif mixer == "Conv":
          self.mixer = ConvMixer(
              dim, num_heads=num_heads, HW=HW, local_k=local_mixer
          )
      else:
          raise TypeError(
              "The mixer must be one of [Global, Local, Conv]"
          )
  
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else Identity()
      )
      if isinstance(norm_layer, str):
          self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
      else:
          self.norm2 = norm_layer(dim)
      mlp_hidden_dim = int(dim * mlp_ratio)
      self.mlp_ratio = mlp_ratio
      self.mlp = Mlp(
          in_features=dim,
          hidden_features=mlp_hidden_dim,
          act_layer=act_layer,
          drop=drop,
      )
      self.prenorm = prenorm
  
imports: from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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, mixer, local_mixer, HW, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, drop_path, act_layer, norm_layer, epsilon, prenorm)