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,
      act_layer=nn.GELU,
      norm_layer="nn.LayerNorm",
      epsilon=1e-5,
  ):
      super().__init__()
      if isinstance(norm_layer, str):
          self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
      elif isinstance(norm_layer, Callable):
          self.norm1 = norm_layer(dim)
      else:
          raise TypeError(
              "The norm_layer must be str or paddle.nn.layer.Layer class"
          )
      self.attn = Attention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          qk_scale=qk_scale,
          attn_drop=attn_drop,
          proj_drop=drop,
      )
      # 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 Identity()
      )
      if isinstance(norm_layer, str):
          self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
      elif isinstance(norm_layer, Callable):
          self.norm2 = norm_layer(dim)
      else:
          raise TypeError(
              "The norm_layer must be str or paddle.nn.layer.Layer class"
          )
      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: from collections.abc import Callable
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from ppdet.core.workspace import register, serializable
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, act_layer, norm_layer, epsilon)