def reset_initialized_parameter(model, include_self=True):
    """
    Reset initialized parameter for [conv, linear, embedding, bn]
    Args:
        model (paddle.Layer): paddle Layer.
        include_self (bool): include_self for Layer.named_sublayers method. Indicate whether to include `model` itself, default to False.
    Return:
        None
    """
    for _, m in model.named_sublayers(include_self=include_self):
        if isinstance(m, nn.Conv2D):
            k = float(m._groups) / (m._in_channels * m._kernel_size[0] *
                                    m._kernel_size[1])
            k = math.sqrt(k)
            _no_grad_uniform_(m.weight, -k, k)
            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
                _no_grad_uniform_(m.bias, -k, k)

        elif isinstance(m, nn.Linear):
            k = math.sqrt(1. / m.weight.shape[0])
            _no_grad_uniform_(m.weight, -k, k)
            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
                _no_grad_uniform_(m.bias, -k, k)

        elif isinstance(m, nn.Embedding):
            _no_grad_normal_(m.weight, mean=0., std=1.)

        elif isinstance(m, (nn.BatchNorm2D, nn.LayerNorm)):
            _no_grad_fill_(m.weight, 1.)
            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
                _no_grad_fill_(m.bias, 0)

    Reset initialized parameter for [conv, linear, embedding, bn]
    Args:
        model (paddle.Layer): paddle Layer.
        include_self (bool): include_self for Layer.named_sublayers method. Indicate whether to include `model` itself, default to False.
    Return:
        None
    
contrib/CrossPseudoSupervision/models/initializer.py