<commit_before>#!/usr/bin/env python3

import torch
import gpytorch
import unittest
from gpytorch.models import ExactGP
from test.models._model_test_case import _ModelTestCase


class ExactGPModel(ExactGP):
    def __init__(self, train_x, train_y, likelihood):
        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

    def forward(self, x):
        mean_x = self.mean_module(x)
        covar_x = self.covar_module(x)
        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)


class TestVariationalGP(_ModelTestCase, unittest.TestCase):
    def create_model(self, train_x, train_y, likelihood):
        model = ExactGPModel(train_x, train_y, likelihood)
        return model

    def create_test_data(self):
        return torch.randn(50, 1)

    def create_likelihood_and_labels(self):
        likelihood = gpytorch.likelihoods.GaussianLikelihood()
        labels = torch.randn(50) + 2
        return likelihood, labels

    def create_batch_test_data(self, batch_size=3):
        return torch.randn(batch_size, 50, 1)<commit_msg>Correct test name<commit_after>#!/usr/bin/env python3

import torch
import gpytorch
import unittest
from gpytorch.models import ExactGP
from test.models._model_test_case import _ModelTestCase


class ExactGPModel(ExactGP):
    def __init__(self, train_x, train_y, likelihood):
        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = gpytorch.means.ConstantMean()
        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())

    def forward(self, x):
        mean_x = self.mean_module(x)
        covar_x = self.covar_module(x)
        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)


class TestExactGP(_ModelTestCase, unittest.TestCase):
    def create_model(self, train_x, train_y, likelihood):
        model = ExactGPModel(train_x, train_y, likelihood)
        return model

    def create_test_data(self):
        return torch.randn(50, 1)

    def create_likelihood_and_labels(self):
        likelihood = gpytorch.likelihoods.GaussianLikelihood()
        labels = torch.randn(50) + 2
        return likelihood, labels

    def create_batch_test_data(self, batch_size=3):
        return torch.randn(batch_size, 50, 1)
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{'dir': 'git-commits-cleaned', 'id': '1854165', 'n_tokens_mistral': 812, 'n_tokens_neox': 787, 'n_words': 155}