def METHOD_NAME(channels):
    value = paddle.rand(
        [bs, value_length, n_heads, channels], dtype=paddle.float32) * 0.01
    sampling_locations = paddle.rand(
        [bs, query_length, n_heads, n_levels, n_points, 2],
        dtype=paddle.float32)
    attention_weights = paddle.rand(
        [bs, query_length, n_heads, n_levels, n_points],
        dtype=paddle.float32) + 1e-5
    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(
        -2, keepdim=True)
    return [value, sampling_locations, attention_weights]