def forward(self, x, mask=None):
        """ Forward function.

        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale

        if config.SDPA_enabled:
            x = torch.nn.functional.scaled_dot_product_attention(
                q, k, v,
                attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
            ).transpose(1, 2).reshape(B_, N, C)
        else:
            attn = (q @ k.transpose(-2, -1))

            relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
            )   # Wh*Ww, Wh*Ww, nH
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

            if mask is not None:
                nW = mask.shape[0]
                attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
                attn = attn.view(-1, self.num_heads, N, N)
                attn = self.softmax(attn)
            else:
                attn = self.softmax(attn)

            attn = self.attn_drop(attn)

            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
 Forward function.

        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        
models/backbones/swin_v1.py