code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      qk_scale=None,
      drop_ratio=0.0,
      attn_drop_ratio=0.0,
      drop_path_ratio=0.0,
      name=None,
  ):
      super(Block, self).__init__(name=name)
      self.norm1 = layers.LayerNormalization(
          epsilon=1e-6, name="LayerNorm_0"
      )
      self.attn = Attention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          qk_scale=qk_scale,
          attn_drop_ratio=attn_drop_ratio,
          proj_drop_ratio=drop_ratio,
          name="MultiHeadAttention",
      )
      # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
      self.drop_path = (
          layers.Dropout(rate=drop_path_ratio, noise_shape=(None, 1, 1))
          if drop_path_ratio > 0.0
          else layers.Activation("linear")
      )
      self.norm2 = layers.LayerNormalization(
          epsilon=1e-6, name="LayerNorm_1"
      )
      self.mlp = MLP(dim, drop=drop_ratio, name="MlpBlock")
  
imports: import tensorflow
  from tensorflow.keras import Model, layers, initializers
  import numpy
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: layers with example: obj = Block(dim, num_heads, qkv_bias, qk_scale, drop_ratio, attn_drop_ratio, drop_path_ratio, name)