code: def __init__(
      self,
      img_size=224,
      patch_size=16,
      embed_dim=768,
      depth=12,
      num_heads=12,
      qkv_bias=True,
      qk_scale=None,
      drop_ratio=0.0,
      attn_drop_ratio=0.0,
      drop_path_ratio=0.0,
      representation_size=None,
      num_classes=1000,
      name="ViT-B/16",
  ):
      super(VisionTransformer, self).__init__(name=name)
      self.num_classes = num_classes
      self.embed_dim = embed_dim
      self.depth = depth
      self.qkv_bias = qkv_bias
  
      self.patch_embed = PatchEmbed(
          img_size=img_size, patch_size=patch_size, embed_dim=embed_dim
      )
      num_patches = self.patch_embed.num_patches
      self.cls_token_pos_embed = ConcatClassTokenAddPosEmbed(
          embed_dim=embed_dim, num_patches=num_patches, name="cls_pos"
      )
  
      self.pos_drop = layers.Dropout(drop_ratio)
  
      dpr = np.linspace(
          0.0, drop_path_ratio, depth
      )  # stochastic depth decay rule
      self.blocks = [
          Block(
              dim=embed_dim,
              num_heads=num_heads,
              qkv_bias=qkv_bias,
              qk_scale=qk_scale,
              drop_ratio=drop_ratio,
              attn_drop_ratio=attn_drop_ratio,
              drop_path_ratio=dpr[i],
              name="encoderblock_{}".format(i),
          )
          for i in range(depth)
      ]
  
      self.norm = layers.LayerNormalization(
          epsilon=1e-6, name="encoder_norm"
      )
  
      if representation_size:
          self.has_logits = True
          self.pre_logits = layers.Dense(
              representation_size, activation="tanh", name="pre_logits"
          )
      else:
          self.has_logits = False
          self.pre_logits = layers.Activation("linear")
  
      self.head = layers.Dense(
          num_classes,
          name="head",
          kernel_initializer=initializers.Zeros(),
      )
  
imports: import tensorflow
  from tensorflow.keras import Model, layers, initializers
  import numpy
label_desc: Use image node_id 1 to create a new VisionTransformer object from inherited base classes: Model with example: obj = VisionTransformer(img_size, patch_size, embed_dim, depth, num_heads, qkv_bias, qk_scale, drop_ratio, attn_drop_ratio, drop_path_ratio, representation_size, num_classes, name)