diff --git a/docs/source/handling_advanced_stypes/handle_text.rst b/docs/source/handling_advanced_stypes/handle_text.rst
index af470416..1328eff2 100644
--- a/docs/source/handling_advanced_stypes/handle_text.rst
+++ b/docs/source/handling_advanced_stypes/handle_text.rst
@@ -345,7 +345,7 @@ Next we need to specify the text model embedding with `LoRA <https://arxiv.org/a
 
 
 As mentioned above, we store text model inputs in the format of dictionary of
-:obj:`~torch_frame.data.MultiNestedTensor`s.
+:obj:`~torch_frame.data.MultiNestedTensor`.
 During the :meth:`forward`, we first transform each
 :obj:`~torch_frame.data.MultiNestedTensor` back to padded PyTorch Tensor by using
 :meth:`~torch_frame.data.MultiNestedTensor.to_dense` with the padding value
diff --git a/test/nn/encoder/test_stypewise_encoder.py b/test/nn/encoder/test_stypewise_encoder.py
index 1d5b893a..e0a70b59 100644
--- a/test/nn/encoder/test_stypewise_encoder.py
+++ b/test/nn/encoder/test_stypewise_encoder.py
@@ -1,6 +1,8 @@
+import copy
+
 import pytest
 
-from torch_frame import stype
+from torch_frame import NAStrategy, stype
 from torch_frame.config import ModelConfig
 from torch_frame.config.text_embedder import TextEmbedderConfig
 from torch_frame.config.text_tokenizer import TextTokenizerConfig
@@ -24,34 +26,46 @@
 )
 
 
-@pytest.mark.parametrize('encoder_cat_cls_kwargs', [(EmbeddingEncoder, {})])
-@pytest.mark.parametrize('encoder_num_cls_kwargs', [
-    (LinearEncoder, {}),
-    (LinearBucketEncoder, {}),
+@pytest.mark.parametrize("encoder_cat_cls_kwargs",
+                         [(EmbeddingEncoder, {
+                             "na_strategy": NAStrategy.MOST_FREQUENT,
+                         })])
+@pytest.mark.parametrize("encoder_num_cls_kwargs", [
+    (LinearEncoder, {
+        "na_strategy": NAStrategy.MEAN,
+    }),
+    (LinearBucketEncoder, {
+        "na_strategy": NAStrategy.MEAN,
+    }),
     (LinearPeriodicEncoder, {
-        'n_bins': 4
+        "n_bins": 4,
+        "na_strategy": NAStrategy.MEAN,
     }),
 ])
-@pytest.mark.parametrize('encoder_multicategorical_cls_kwargs', [
-    (MultiCategoricalEmbeddingEncoder, {}),
+@pytest.mark.parametrize("encoder_multicategorical_cls_kwargs", [
+    (MultiCategoricalEmbeddingEncoder, {
+        "na_strategy": NAStrategy.ZEROS
+    }),
 ])
-@pytest.mark.parametrize('encoder_timestamp_cls_kwargs', [
-    (TimestampEncoder, {}),
+@pytest.mark.parametrize("encoder_timestamp_cls_kwargs", [
+    (TimestampEncoder, {
+        "na_strategy": NAStrategy.MEDIAN_TIMESTAMP
+    }),
 ])
-@pytest.mark.parametrize('encoder_text_embedded_cls_kwargs', [
+@pytest.mark.parametrize("encoder_text_embedded_cls_kwargs", [
     (LinearEmbeddingEncoder, {}),
 ])
-@pytest.mark.parametrize('encoder_text_tokenized_cls_kwargs', [
+@pytest.mark.parametrize("encoder_text_tokenized_cls_kwargs", [
     (LinearModelEncoder, {
-        'col_to_model_cfg': {
-            'text_tokenized_1':
+        "col_to_model_cfg": {
+            "text_tokenized_1":
             ModelConfig(model=RandomTextModel(12), out_channels=12),
-            'text_tokenized_2':
+            "text_tokenized_2":
             ModelConfig(model=RandomTextModel(6), out_channels=6)
         },
     }),
 ])
-@pytest.mark.parametrize('encoder_embedding_cls_kwargs', [
+@pytest.mark.parametrize("encoder_embedding_cls_kwargs", [
     (LinearEmbeddingEncoder, {}),
 ])
 def test_stypewise_feature_encoder(
@@ -66,7 +80,7 @@ def test_stypewise_feature_encoder(
     num_rows = 10
     dataset: Dataset = FakeDataset(
         num_rows=num_rows,
-        with_nan=False,
+        with_nan=True,
         stypes=[
             stype.categorical,
             stype.numerical,
@@ -124,7 +138,12 @@ def test_stypewise_feature_encoder(
         col_names_dict=tensor_frame.col_names_dict,
         stype_encoder_dict=stype_encoder_dict,
     )
+    tensor_frame_original = copy.deepcopy(tensor_frame)
     x, col_names = encoder(tensor_frame)
+
+    # Test no in-place operation in encoder
+    assert tensor_frame_original == tensor_frame
+
     assert x.shape == (num_rows, tensor_frame.num_cols, out_channels)
     assert col_names == [
         "num_1",
@@ -138,9 +157,9 @@ def test_stypewise_feature_encoder(
         "multicat_2",
         "multicat_3",
         "multicat_4",
-        'timestamp_0',
-        'timestamp_1',
-        'timestamp_2',
+        "timestamp_0",
+        "timestamp_1",
+        "timestamp_2",
         "emb_1",
         "emb_2",
         "text_embedded_1",
diff --git a/torch_frame/data/tensor_frame.py b/torch_frame/data/tensor_frame.py
index c55dbac4..72c76b83 100644
--- a/torch_frame/data/tensor_frame.py
+++ b/torch_frame/data/tensor_frame.py
@@ -223,7 +223,7 @@ def __eq__(self, other: Any) -> bool:
                     return False
                 if self_feat.shape != other_feat.shape:
                     return False
-                if not torch.allclose(self_feat, other_feat):
+                if not torch.allclose(self_feat, other_feat, equal_nan=True):
                     return False
             elif isinstance(self_feat, MultiNestedTensor):
                 if not isinstance(other_feat, MultiNestedTensor):