diff --git a/tests/pytorch_tests/model_tests/feature_models/bn_folding_test.py b/tests/pytorch_tests/model_tests/feature_models/bn_folding_test.py
index bf3edb67e..ed217a5c7 100644
--- a/tests/pytorch_tests/model_tests/feature_models/bn_folding_test.py
+++ b/tests/pytorch_tests/model_tests/feature_models/bn_folding_test.py
@@ -85,12 +85,7 @@ def compare(self, quantized_models, float_model, input_x=None, quantization_info
 
         is_bn_in_model = nn.BatchNorm2d in [type(module) for name, module in quant_model.named_modules()]
         self.unit_test.assertTrue(self.fold_applied is not is_bn_in_model)
-
-        # TODO: remove this and set atol=1e-5 for all tests after fixing the issue with ConvTranspose2d.
-        # https://github.com/sony/model_optimization/issues/1115
-        is_convtranspose2d_in_model = nn.ConvTranspose2d in [type(module) for name, module in quant_model.named_modules()]
-        atol = 1e-3 if is_convtranspose2d_in_model else 1e-5
-        self.unit_test.assertTrue(np.isclose(out_quant, out_float, atol=atol, rtol=1e-4).all())
+        self.unit_test.assertTrue(np.isclose(out_quant, out_float, atol=1e-5, rtol=1e-4).all())
 
 
 class BNForwardFoldingNet(nn.Module):