yeandy commented on code in PR #17196:
URL: https://github.com/apache/beam/pull/17196#discussion_r845149575


##########
sdks/python/apache_beam/ml/inference/pytorch_impl.py:
##########
@@ -0,0 +1,119 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# pytype: skip-file
+
+from typing import Iterable
+from typing import List
+from typing import Union
+
+import numpy as np
+import pickle
+import torch
+from torch import nn
+
+from apache_beam.ml.inference.base import InferenceRunner
+from apache_beam.ml.inference.base import ModelLoader
+
+
+class PytorchInferenceRunner(InferenceRunner):
+  """
+  Implements Pytorch inference method
+  """
+  def __init__(self, input_dim: int, device: torch.device):
+    self._input_dim = input_dim
+    self._device = device
+
+  def run_inference(
+      self, batch: List[Union[np.ndarray, torch.Tensor]],
+      model: nn.Module) -> Iterable[torch.Tensor]:
+    """
+    Runs inferences on a batch of examples and returns an Iterable of
+    Predictions."""
+    if batch:
+      if isinstance(batch[0], np.ndarray):
+        batch = torch.Tensor(batch)
+      elif isinstance(batch[0], torch.Tensor):
+        batch = torch.stack(batch)
+      else:
+        raise ValueError("PCollection must be an numpy array or a torch 
Tensor")

Review Comment:
   Good point. But per your previous comment (assume type is already Tensor), 
I've removed this section. 



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