def METHOD_NAME(self, x):
    input_shape = ops.shape(x)
    H, W = int(math.sqrt(input_shape[1])), int(math.sqrt(input_shape[1]))
    B, C = input_shape[0], input_shape[2]
    q = self.q(x)
    q = ops.reshape(
        q,
        (
            input_shape[0],
            input_shape[1],
            self.num_heads,
            input_shape[2] // self.num_heads,
        ),
    )
    q = ops.transpose(q, [0, 2, 1, 3])
    if self.sr_ratio > 1:
        x = ops.reshape(
            ops.transpose(x, [0, 2, 1]),
            (B, H, W, C),
        )
        x = self.sr(x)
        x = ops.reshape(x, [input_shape[0], input_shape[2], -1])
        x = ops.transpose(x, [0, 2, 1])
        x = self.norm(x)
    k = self.k(x)
    v = self.v(x)
    k = ops.transpose(
        ops.reshape(
            k,
            [B, -1, self.num_heads, C // self.num_heads],
        ),
        [0, 2, 1, 3],
    )
    v = ops.transpose(
        ops.reshape(
            v,
            [B, -1, self.num_heads, C // self.num_heads],
        ),
        [0, 2, 1, 3],
    )
    attn = (q @ ops.transpose(k, [0, 1, 3, 2])) * self.scale
    attn = ops.nn.softmax(attn, axis=-1)
    attn = attn @ v
    attn = ops.reshape(
        ops.transpose(attn, [0, 2, 1, 3]),
        [input_shape[0], input_shape[1], input_shape[2]],
    )
    x = self.proj(attn)
    return x