got the wrong result when using paconvet

• pytorch

import torch.nn as nn
from functools import partial


class test(nn.Module):
    def __init__(self, in_channels, out_channels, norm_func=nn.LayerNorm):
        super(test, self).__init__()
        self.norm = norm_func(in_channels)
        self.linear = nn.Linear(in_channels, out_channels)
    
    def forward(self, x):
        x = self.norm(x)
        x = self.linear(x)
        return x

if __name__ == "__main__":
    model = test(10, 10, partial(nn.LayerNorm, eps=0.2))

• paddle

import paddle
from functools import partial


class test(paddle.nn.Layer):

    def __init__(self, in_channels, out_channels, norm_func=paddle.nn.LayerNorm
        ):
        super(test, self).__init__()
        self.norm = norm_func(in_channels)
        self.linear = paddle.nn.Linear(in_features=in_channels,
            out_features=out_channels)

    def forward(self, x):
        x = self.norm(x)
        x = self.linear(x)
        return x


if __name__ == '__main__':
    model = test(10, 10, partial(paddle.nn.LayerNorm, eps=0.2))

Traceback (most recent call last):
  File "/home/greatx/repos/PaConvert/paddle_project/test.py", line 21, in <module>
    model = test(10, 10, partial(paddle.nn.LayerNorm, eps=0.2))
  File "/home/greatx/repos/PaConvert/paddle_project/test.py", line 10, in __init__
    self.norm = norm_func(in_channels)
TypeError: LayerNorm.__init__() got an unexpected keyword argument 'eps'

eps should be converted to epsilon.

Start by locating the PaConvert conversion rule that handles LayerNorm arguments and the handling of functools.partial. Reproduce the provided Python example, then verify that the converted Paddle call accepts the equivalent epsilon argument and that the original TypeError no longer occurs.