[BUG]: fix(aptatrans) removing torch.squeeze(x, dim=2) (#446)

#### Reference Issues/PRs

Example: Fixes #439 



#### What does this implement/fix? Explain your changes.
Fixed a one-line bug in AptaTrans.forward() where torch.squeeze(out,
dim=2) is removed and adde tests.
  
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---------

Signed-off-by: RITANKAR SAHA <ritankar.saha786@gmail.com>

Author: ritankarsaha

pyaptamer/aptatrans/_model.py

@@ -369,7 +369,6 @@ def forward(self, x_apta: Tensor, x_prot: Tensor) -> Tensor:
         """
         out = self.forward_imap(x_apta, x_prot)
 
-        out = torch.squeeze(out, dim=2)  # remove extra dimension
         out = self.gelu1(self.bn1(self.conv1(out)))
         out = self.layer1(out)
         out = self.layer2(out)

pyaptamer/aptatrans/tests/test_aptatrans.py

@@ -159,6 +159,49 @@ def test_forward(
         assert torch.all(output >= 0.0) and torch.all(output <= 1.0)
         assert not torch.allclose(output[0], output[1], atol=1e-5)
 
+    @pytest.mark.parametrize(
+        "batch_size, seq_len_apta, seq_len_prot, in_dim",
+        [
+            # Asymmetric sequence lengths
+            (2, 10, 20, 32),
+            (3, 20, 10, 32),
+            (4, 15, 25, 64),
+        ],
+    )
+    @torch.no_grad()
+    def test_forward_output_shape_asymmetric(
+        self, batch_size: int, seq_len_apta: int, seq_len_prot: int, in_dim: int
+    ) -> None:
+        """Check forward() output is (batch, 1) for asymmetric sequence lengths.
+
+        Regression test for the spurious squeeze(dim=2) in forward(): the interaction
+        map from forward_imap is already (batch, 1, s1, s2) — the shape conv1 expects.
+        """
+        apta_embedding = EncoderPredictorConfig(
+            num_embeddings=16, target_dim=8, max_len=seq_len_apta
+        )
+        prot_embedding = EncoderPredictorConfig(
+            num_embeddings=16, target_dim=8, max_len=seq_len_prot
+        )
+        model = AptaTrans(
+            apta_embedding=apta_embedding,
+            prot_embedding=prot_embedding,
+            in_dim=in_dim,
+            n_encoder_layers=2,
+            n_heads=4,
+            conv_layers=[2, 2, 2],
+        )
+
+        x_apta = torch.randint(0, 16, (batch_size, seq_len_apta), dtype=torch.long)
+        x_prot = torch.randint(0, 16, (batch_size, seq_len_prot), dtype=torch.long)
+
+        output = model(x_apta, x_prot)
+
+        assert output.shape == (batch_size, 1), (
+            f"Expected ({batch_size}, 1), got {tuple(output.shape)}. "
+            f"seq_len_apta={seq_len_apta}, seq_len_prot={seq_len_prot}"
+        )
+
 
 class MockAptaTransNeuralNet(nn.Module):
     """Mock AptaTrans model for testing pipeline."""