code: def multi_head_attention_forward(
      x: Tensor,
      num_heads: int,
      q_proj: Linear,
      k_proj: Linear,
      v_proj: Linear,
      c_proj: Linear,
      attn_mask: Optional[Tensor] = None,
  ):
      max_len, batch_size, emb_dim = x.shape
      head_dim = emb_dim // num_heads
      scaling = float(head_dim) ** -0.5
      q = q_proj(x)  # L, N, E
      k = k_proj(x)  # L, N, E
      v = v_proj(x)  # L, N, E
      # k = k.con
      v = v.reshape((-1, batch_size * num_heads, head_dim)).transpose(
          (1, 0, 2)
      )
      k = k.reshape((-1, batch_size * num_heads, head_dim)).transpose(
          (1, 0, 2)
      )
      q = q.reshape((-1, batch_size * num_heads, head_dim)).transpose(
          (1, 0, 2)
      )
  
      q = q * scaling
      qk = paddle.bmm(q, k.transpose((0, 2, 1)))
      if attn_mask is not None:
          if attn_mask.ndim == 2:
              attn_mask.unsqueeze_(0)
          # assert str(attn_mask.dtype) == 'VarType.FP32' and attn_mask.ndim == 3
          assert (
              attn_mask.shape[0] == 1
              and attn_mask.shape[1] == max_len
              and attn_mask.shape[2] == max_len
          )
          qk += attn_mask
  
      qk = paddle.nn.functional.softmax(qk, axis=-1)
      atten = paddle.bmm(qk, v)
      atten = atten.transpose((1, 0, 2))
      atten = atten.reshape((max_len, batch_size, emb_dim))
      atten = c_proj(atten)
      return atten
  
imports: from typing import Optional
  import paddle
  import paddle.nn
  from paddle import Tensor
  from paddle.nn import functional
  from paddle.nn import Linear
label_desc: Use image node_id 1 for calling a global function with example usage: multi_head_attention_forward(x, num_heads, q_proj, k_proj, v_proj, c_proj, attn_mask) and returns: atten