code: def forward(
      self,
      query,
      key,
      value,
      key_padding_mask=None,
      need_weights=True,
      attn_mask=None,
  ):
      if not self._qkv_same_embed_dim:
          return _F.multi_head_attention_forward(
              query,
              key,
              value,
              self.embed_dim,
              self.num_heads,
              self.in_proj_weight,
              self.in_proj_bias,
              self.bias_k,
              self.bias_v,
              self.add_zero_attn,
              self.dropout,
              self.out_proj.weight_fake_quant(self.out_proj.weight),
              self.out_proj.bias,
              training=self.training,
              key_padding_mask=key_padding_mask,
              need_weights=need_weights,
              attn_mask=attn_mask,
              use_separate_proj_weight=True,
              q_proj_weight=self.q_proj_weight_fake_quant(
                  self.q_proj_weight
              ),
              k_proj_weight=self.k_proj_weight_fake_quant(
                  self.k_proj_weight
              ),
              v_proj_weight=self.v_proj_weight_fake_quant(
                  self.v_proj_weight
              ),
          )
      else:
          return _F.multi_head_attention_forward(
              query,
              key,
              value,
              self.embed_dim,
              self.num_heads,
              self.in_proj_weight_fake_quant(self.in_proj_weight),
              self.in_proj_bias,
              self.bias_k,
              self.bias_v,
              self.add_zero_attn,
              self.dropout,
              self.out_proj.weight_fake_quant(self.out_proj.weight),
              self.out_proj.bias,
              training=self.training,
              key_padding_mask=key_padding_mask,
              need_weights=need_weights,
              attn_mask=attn_mask,
          )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .palettization_config import SUPPORTED_PYTORCH_QAT_MODULES
label_desc: Use image node_id 1 for calling the MultiheadAttention obj's underlying member method code with example usage: obj.forward(query, key, value, key_padding_mask, need_weights, attn_mask) and returns: _F, _F