def linear_quantize(input, scale, zero_point, inplace=False):
    """
    Quantize single-precision input tensor to integers with the given scaling factor and zeropoint.

    Args:
        input (`torch.Tensor`):
            Single-precision input tensor to be quantized.
        scale (`torch.Tensor`):
            Scaling factor for quantization.
        zero_pint (`torch.Tensor`):
            Shift for quantization.
        inplace (`bool`, *optional*, defaults to `False`):
            Whether to compute inplace or not.

    Returns:
        `torch.Tensor`: Linearly quantized value of *input* according to *scale* and *zero_point*.
    """
    # reshape scale and zeropoint for convolutional weights and activation
    if len(input.shape) == 4:
        scale = scale.view(-1, 1, 1, 1)
        zero_point = zero_point.view(-1, 1, 1, 1)
    # reshape scale and zeropoint for linear weights
    elif len(input.shape) == 2:
        scale = scale.view(-1, 1)
        zero_point = zero_point.view(-1, 1)
    else:
        scale = scale.view(-1)
        zero_point = zero_point.view(-1)
    # quantized = float / scale + zero_point
    if inplace:
        input.mul_(1.0 / scale).add_(zero_point).round_()
        return input
    return torch.round(1.0 / scale * input + zero_point)

    Quantize single-precision input tensor to integers with the given scaling factor and zeropoint.

    Args:
        input (`torch.Tensor`):
            Single-precision input tensor to be quantized.
        scale (`torch.Tensor`):
            Scaling factor for quantization.
        zero_pint (`torch.Tensor`):
            Shift for quantization.
        inplace (`bool`, *optional*, defaults to `False`):
            Whether to compute inplace or not.

    Returns:
        `torch.Tensor`: Linearly quantized value of *input* according to *scale* and *zero_point*.
    
dpkd/transformers/src/transformers/models/ibert/quant_modules.py