AnandInguva commented on code in PR #21868:
URL: https://github.com/apache/beam/pull/21868#discussion_r898290184
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sdks/python/apache_beam/ml/inference/pytorch_inference.py:
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@@ -190,6 +194,10 @@ def run_inference(
For the same key across all examples, this will stack all Tensors values
in a vectorized format to optimize the inference call.
+ Args:
+ batch: A sequence of Tensors.
+ model: A pytorch model.
Review Comment:
Do we need to explicitly say `Must implement forward(X)`?
Also Users passes X as `dict` when the forward call of the model requires
multiple positional arguments. If `X` is a `dict`, we unpack the positional
arguments to the forward call in RunInference API by calling `model(**X)` which
implicitly means `model.forward(**X)`. I think line 200 would confuse the users
on this behavior.
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