import torch
from torch import nn


class Net(nn.Module):
    # optimizer = None
    losses = []

    def __init__(self, n_feature, n_neurons, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_neurons)  # neurons layer
        self.hidden2 = torch.nn.Linear(n_neurons, n_neurons)  # neurons layer
        self.predict = torch.nn.Linear(n_neurons, n_output)  # output layer

    def forward(self, x):
        x = torch.tanh(self.hidden(x))  # activation function for neurons layer
        x = torch.tanh(self.hidden2(x))  # activation function for neurons layer
        x = self.predict(x)  # linear output
        return x
486a10bba800a48e9f1f749e23f5984cc9baed20
<mask token>


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

    def forward(self, x):
        x = torch.tanh(self.hidden(x))
        x = torch.tanh(self.hidden2(x))
        x = self.predict(x)
        return x

<mask token>


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

    def __init__(self, n_feature, n_neurons, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_neurons)
        self.hidden2 = torch.nn.Linear(n_neurons, n_neurons)
        self.predict = torch.nn.Linear(n_neurons, n_output)

    def forward(self, x):
        x = torch.tanh(self.hidden(x))
        x = torch.tanh(self.hidden2(x))
        x = self.predict(x)
        return x

<mask token>


class Net(nn.Module):
    losses = []

    def __init__(self, n_feature, n_neurons, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_neurons)
        self.hidden2 = torch.nn.Linear(n_neurons, n_neurons)
        self.predict = torch.nn.Linear(n_neurons, n_output)

    def forward(self, x):
        x = torch.tanh(self.hidden(x))
        x = torch.tanh(self.hidden2(x))
        x = self.predict(x)
        return x

import torch
from torch import nn


class Net(nn.Module):
    losses = []

    def __init__(self, n_feature, n_neurons, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_neurons)
        self.hidden2 = torch.nn.Linear(n_neurons, n_neurons)
        self.predict = torch.nn.Linear(n_neurons, n_output)

    def forward(self, x):
        x = torch.tanh(self.hidden(x))
        x = torch.tanh(self.hidden2(x))
        x = self.predict(x)
        return x

import torch
from torch import nn


class Net(nn.Module):
    # optimizer = None
    losses = []

    def __init__(self, n_feature, n_neurons, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_neurons)  # neurons layer
        self.hidden2 = torch.nn.Linear(n_neurons, n_neurons)  # neurons layer
        self.predict = torch.nn.Linear(n_neurons, n_output)  # output layer

    def forward(self, x):
        x = torch.tanh(self.hidden(x))  # activation function for neurons layer
        x = torch.tanh(self.hidden2(x))  # activation function for neurons layer
        x = self.predict(x)  # linear output
        return x