def METHOD_NAME():
    from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerSelfOutput
    def forward(self: Blip2QFormerSelfOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
    return forward