code: def call(self, inputs, training=None):
      # [batch_size, num_patches + 1, total_embed_dim]
      B, N, C = inputs.shape
  
      # qkv(): -> [batch_size, num_patches + 1, 3 * total_embed_dim]
      qkv = self.qkv(inputs)
      # reshape: -> [batch_size, num_patches + 1, 3, num_heads, embed_dim_per_head]
      qkv = tf.reshape(
          qkv, [B, N, 3, self.num_heads, C // self.num_heads]
      )
      # transpose: -> [3, batch_size, num_heads, num_patches + 1, embed_dim_per_head]
      qkv = tf.transpose(qkv, [2, 0, 3, 1, 4])
      # [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
      q, k, v = qkv[0], qkv[1], qkv[2]
  
      # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches + 1]
      # multiply -> [batch_size, num_heads, num_patches + 1, num_patches + 1]
      attn = tf.matmul(a=q, b=k, transpose_b=True) * self.scale
      attn = tf.nn.softmax(attn, axis=-1)
      attn = self.attn_drop(attn, training=training)
  
      # multiply -> [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
      x = tf.matmul(attn, v)
      # transpose: -> [batch_size, num_patches + 1, num_heads, embed_dim_per_head]
      x = tf.transpose(x, [0, 2, 1, 3])
      # reshape: -> [batch_size, num_patches + 1, total_embed_dim]
      x = tf.reshape(x, [B, N, C])
  
      x = self.proj(x)
      x = self.proj_drop(x, training=training)
      return x
  
imports: import tensorflow
  from tensorflow.keras import Model, layers, initializers
  import numpy
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.call(inputs, training) and returns: x