import torch
from torch import nn


class neural_network(nn.Module):

    def __init__(self):
        super(neural_network, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=5, num_layers=1,
            batch_first=True)
        self.fc1 = nn.Linear(in_features=5, out_features=1)

    def forward(self, x):
        output, _status = self.lstm(x)
        output = output[:, -1, :]
        output = self.fc1(torch.relu(output))
        return output

d18c387ad7c27c36c48e64c5086c37e565546f64
<mask token>


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

<mask token>


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

    def forward(self, x):
        output, _status = self.lstm(x)
        output = output[:, -1, :]
        output = self.fc1(torch.relu(output))
        return output

<mask token>


class neural_network(nn.Module):

    def __init__(self):
        super(neural_network, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=5, num_layers=1,
            batch_first=True)
        self.fc1 = nn.Linear(in_features=5, out_features=1)

    def forward(self, x):
        output, _status = self.lstm(x)
        output = output[:, -1, :]
        output = self.fc1(torch.relu(output))
        return output

import torch
from torch import nn


class neural_network(nn.Module):

    def __init__(self):
        super(neural_network, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=5, num_layers=1,
            batch_first=True)
        self.fc1 = nn.Linear(in_features=5, out_features=1)

    def forward(self, x):
        output, _status = self.lstm(x)
        output = output[:, -1, :]
        output = self.fc1(torch.relu(output))
        return output
