def batch_frexp(inputs, max_bit=31):
    """
    Decompose the scaling factor into mantissa and twos exponent.

    Args:
        scaling_factor (`torch.Tensor`):
            Target scaling factor to decompose.

    Returns:
        ``Tuple(torch.Tensor, torch.Tensor)`: mantisa and exponent
    """

    shape_of_input = inputs.size()

    # trans the input to be a 1-d tensor
    inputs = inputs.view(-1)

    output_m, output_e = np.frexp(inputs.cpu().numpy())
    tmp_m = []
    for m in output_m:
        int_m_shifted = int(
            decimal.Decimal(m * (2**max_bit)).quantize(decimal.Decimal("1"), rounding=decimal.ROUND_HALF_UP)
        )
        tmp_m.append(int_m_shifted)
    output_m = np.array(tmp_m)

    output_e = float(max_bit) - output_e

    return (
        torch.from_numpy(output_m).to(inputs.device).view(shape_of_input),
        torch.from_numpy(output_e).to(inputs.device).view(shape_of_input),
    )

    Decompose the scaling factor into mantissa and twos exponent.

    Args:
        scaling_factor (`torch.Tensor`):
            Target scaling factor to decompose.

    Returns:
        ``Tuple(torch.Tensor, torch.Tensor)`: mantisa and exponent
    
dpkd/transformers/src/transformers/models/ibert/quant_modules.py