diff --git a/tests/ratio_estimator_test.py b/tests/ratio_estimator_test.py
new file mode 100644
index 000000000..e10ec6fdf
--- /dev/null
+++ b/tests/ratio_estimator_test.py
@@ -0,0 +1,63 @@
+# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
+# under the Affero General Public License v3, see <https://www.gnu.org/licenses/>.
+
+from __future__ import annotations
+
+import pytest
+import torch
+from torch import eye, zeros
+from torch.distributions import MultivariateNormal
+
+from sbi.neural_nets.classifier import build_linear_classifier
+from sbi.neural_nets.ratio_estimators import TensorRatioEstimator
+
+
+class EmbeddingNet(torch.nn.Module):
+    def __init__(self, shape: torch.Size) -> None:
+        super().__init__()
+        self.shape = shape
+
+    def forward(self, x):
+        for _ in range(len(self.shape) - 1):
+            x = torch.sum(x, dim=-1)
+        return x
+
+
+@pytest.mark.parametrize("ratio_estimator", (TensorRatioEstimator,))
+@pytest.mark.parametrize(
+    "theta_shape", ((1,), (2,), (1, 1), (2, 2), (1, 1, 1), (2, 2, 2))
+)
+@pytest.mark.parametrize("x_shape", ((1,), (2,), (1, 1), (2, 2), (1, 1, 1), (2, 2, 2)))
+def test_api_ratio_estimator(ratio_estimator, theta_shape, x_shape):
+    r"""Checks whether we can evaluate ratio estimators correctly.
+
+    Args:
+        ratio_estimator: RatioEstimator subclass.
+        input_dim: Dimensionality of the input.
+    """
+
+    nsamples = 10
+
+    theta_mvn = MultivariateNormal(
+        loc=zeros(*theta_shape), covariance_matrix=eye(theta_shape[-1])
+    )
+    batch_theta = theta_mvn.sample((nsamples,))
+    x_mvn = MultivariateNormal(loc=zeros(*x_shape), covariance_matrix=eye(x_shape[-1]))
+    batch_x = x_mvn.sample((nsamples,))
+
+    if ratio_estimator == TensorRatioEstimator:
+        estimator = build_linear_classifier(
+            batch_x=batch_theta,
+            batch_y=batch_x,
+            embedding_net_x=EmbeddingNet(theta_shape),
+            embedding_net_y=EmbeddingNet(x_shape),
+        )
+    else:
+        raise NotImplementedError()
+
+    # forward computes the unnormalized_log_ratio
+    # calling all other methods in the process
+    unnormalized_log_ratio = estimator(batch_theta, batch_x)
+    assert (
+        unnormalized_log_ratio.shape == (nsamples,)
+    ), f"unnormalized_log_ratio shape is not correct. It is of shape {unnormalized_log_ratio.shape}, but should be {(nsamples,)}"