code: def forward(self, x):
      self.training = True
      B, N, C = x.shape
      qkv = self.qkv(x)
      qkv = paddle.reshape(
          qkv, [B, N, self.num_heads, self.h // self.num_heads]
      )
      q, k, v = paddle.split(
          qkv, [self.key_dim, self.key_dim, self.d], axis=3
      )
      q = paddle.transpose(q, perm=[0, 2, 1, 3])
      k = paddle.transpose(k, perm=[0, 2, 1, 3])
      v = paddle.transpose(v, perm=[0, 2, 1, 3])
      k_transpose = paddle.transpose(k, perm=[0, 1, 3, 2])
  
      if self.training:
          attention_biases = cal_attention_biases(
              self.attention_biases, self.attention_bias_idxs
          )
      else:
          attention_biases = self.ab
      attn = (
          paddle.matmul(q, k_transpose) * self.scale + attention_biases
      )
      attn = F.softmax(attn)
      x = paddle.transpose(paddle.matmul(attn, v), perm=[0, 2, 1, 3])
      x = paddle.reshape(x, [B, N, self.dh])
      x = self.proj(x)
      return x
  
imports: import itertools
  import math
  import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import TruncatedNormal
  from paddle.regularizer import L2Decay
  from .vision_transformer import Identity
  from .vision_transformer import ones_
  from .vision_transformer import trunc_normal_
  from .vision_transformer import zeros_
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x