def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    """Reference: https://people.sc.fsu.edu/~jburkardt/presentations
    /truncated_normal.pdf"""

    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
            'The distribution of values may be incorrect.',
            stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        lower_bound = norm_cdf((a - mean) / std)
        upper_bound = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [l, u], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * lower_bound - 1, 2 * upper_bound - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor
Reference: https://people.sc.fsu.edu/~jburkardt/presentations
    /truncated_normal.pdf
ControlNet-v1-1-nightly/annotator/uniformer/mmseg/models/utils/weight_init.py