import torch
import torch.nn as nn


class Langevin_Layer(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(Langevin_Layer, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.weights = nn.Parameter(torch.Tensor(self.input_dim, self.
            output_dim).uniform_(-0.01, 0.01))
        self.biases = nn.Parameter(torch.Tensor(self.output_dim).uniform_(-
            0.01, 0.01))

    def forward(self, x):
        return torch.mm(x, self.weights) + self.biases

b8e68a31fe6273de5e5bf2251d28f81d18fcf6fb
<mask token>


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

<mask token>


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

    def forward(self, x):
        return torch.mm(x, self.weights) + self.biases

<mask token>


class Langevin_Layer(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(Langevin_Layer, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.weights = nn.Parameter(torch.Tensor(self.input_dim, self.
            output_dim).uniform_(-0.01, 0.01))
        self.biases = nn.Parameter(torch.Tensor(self.output_dim).uniform_(-
            0.01, 0.01))

    def forward(self, x):
        return torch.mm(x, self.weights) + self.biases

import torch
import torch.nn as nn


class Langevin_Layer(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(Langevin_Layer, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.weights = nn.Parameter(torch.Tensor(self.input_dim, self.
            output_dim).uniform_(-0.01, 0.01))
        self.biases = nn.Parameter(torch.Tensor(self.output_dim).uniform_(-
            0.01, 0.01))

    def forward(self, x):
        return torch.mm(x, self.weights) + self.biases
