def dequantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.Tensor] = None) -> Tensor:
    """
    Dequantizes the 8-bit tensor to 32-bit.

    Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via
    the quantization map `code`.

    Parameters
    ----------
    A : torch.Tensor
        The 8-bit input tensor.
    code : torch.Tensor
        The quantization map.
    out : torch.Tensor
        The 32-bit output tensor.

    Returns
    -------
    torch.Tensor:
        32-bit output tensor.
    """
    with _cuda_device_of(A):
        if out is None:
            out = torch.zeros_like(A, dtype=torch.float32)
        is_on_gpu([code, A, out])
        stream = _get_tensor_stream(A)
        lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()), stream)

    return out

    Dequantizes the 8-bit tensor to 32-bit.

    Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via
    the quantization map `code`.

    Parameters
    ----------
    A : torch.Tensor
        The 8-bit input tensor.
    code : torch.Tensor
        The quantization map.
    out : torch.Tensor
        The 32-bit output tensor.

    Returns
    -------
    torch.Tensor:
        32-bit output tensor.
    
bitsandbytes/functional.py