[2] Review comments handled

python/tvm/relay/frontend/tensorflow.py

@@ -932,11 +932,16 @@ def _impl(inputs, attr, params, mod):
         ret = _op.nn.sparse_dense(data, [weight_data, weight_indices, weight_indptrs])
 
         # If both are true means First input was dense and second was sparse
-        # TODO: Support other adjoint option too
+        # TODO(ANSHUMAN87): Support other adjoint option too
         if attr.get("adjoint_a") and attr.get("adjoint_b"):
             ret = _op.transpose(ret)
         else:
-            raise tvm.error.OpAttributeUnImplemented("Adjoint option is not supported yet.")
+            raise tvm.error.OpAttributeUnImplemented(
+                "Only tf.sparse.sparse_dense_matmul() with adjoint_a=True and adjoint_b=True"
+                " is supported, but adjoint_a={} and adjoint_b={} was supplied.".format(
+                    attr.get("adjoint_a"), attr.get("adjoint_b")
+                )
+            )
 
         return ret
 

tests/python/frontend/tensorflow/test_forward.py

@@ -1758,7 +1758,7 @@ def test_forward_batch_matmul():
 def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=False):
     """ One iteration of sparse_dense_matmul """
 
-    # TODO: Support adjoint options too
+    # TODO(ANSHUMAN87): Support adjoint options too
     for adjoint_a in [False]:
         for adjoint_b in [False]:
             with tf.Graph().as_default():
@@ -1776,7 +1776,7 @@ def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=Fal
 
                 B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
 
-                # TODO: There is an issue in cuda scheduling for csr, work in progress
+                # TODO(ANSHUMAN87): There is an issue in cuda scheduling for csr, work in progress
                 compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
 
 
@@ -1794,7 +1794,7 @@ def test_forward_sparse_dense_matmul():
     #
     # ------------------------------------------------------------------
 
-    # TODO: False case for flip need to be supported
+    # TODO(ANSHUMAN87): False case for flip need to be supported
     # _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], [4, 3], "float32")
     _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 5], [4, 3], "float32", True)
     _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 3], [3, 3], "float32", True)