import torch
import torch.nn as nn


class Normalize_layer(nn.Module):

    def __init__(self, mean, std):
        super(Normalize_layer, self).__init__()
        self.mean = nn.Parameter(torch.Tensor(mean).unsqueeze(1).unsqueeze(
            1), requires_grad=False)
        self.std = nn.Parameter(torch.Tensor(std).unsqueeze(1).unsqueeze(1),
            requires_grad=False)

    def forward(self, x):
        return x.sub(self.mean).div(self.std)

331c4f2be63956276599cf3ad8c77edaa155b409
<mask token>


class Normalize_layer(nn.Module):
    <mask token>
    <mask token>

<mask token>


class Normalize_layer(nn.Module):
    <mask token>

    def forward(self, x):
        return x.sub(self.mean).div(self.std)

<mask token>


class Normalize_layer(nn.Module):

    def __init__(self, mean, std):
        super(Normalize_layer, self).__init__()
        self.mean = nn.Parameter(torch.Tensor(mean).unsqueeze(1).unsqueeze(
            1), requires_grad=False)
        self.std = nn.Parameter(torch.Tensor(std).unsqueeze(1).unsqueeze(1),
            requires_grad=False)

    def forward(self, x):
        return x.sub(self.mean).div(self.std)

import torch
import torch.nn as nn


class Normalize_layer(nn.Module):

    def __init__(self, mean, std):
        super(Normalize_layer, self).__init__()
        self.mean = nn.Parameter(torch.Tensor(mean).unsqueeze(1).unsqueeze(
            1), requires_grad=False)
        self.std = nn.Parameter(torch.Tensor(std).unsqueeze(1).unsqueeze(1),
            requires_grad=False)

    def forward(self, x):
        return x.sub(self.mean).div(self.std)
