samskalicky commented on a change in pull request #19386:
URL: https://github.com/apache/incubator-mxnet/pull/19386#discussion_r523375529
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File path: example/extensions/lib_subgraph/README.md
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@@ -107,15 +107,15 @@ The `optimize_for` API takes at least 1 argument,
`backend` which is a string th
For the Gluon API, `hybridize` can be called on HybridBlocks to partition the
internal CachedOp Symbol.
```python
-block.hybridize(backend=None, backend_opts=None, clear=True, **kwargs)
+block.hybridize(backend=None, clear=True, **kwargs)
```
-The `hybridize` function prepares the HybridBlock to be converted into a
backend symbol. The `backend` argument is a string that identifies which
backend that will partition the model. The `backend_opts` are other
user-specified options (as a Python dictionary of strings mapped to strings)
that will be passed to the backend partitioning APIs. The `clear` argument
defaults to `True` and clears any previous optimizations done on the block. If
you want to chain optimizations together, set `clear` to `False`. The actual
partitioning takes place during the forward pass. If you want to use
`hybridize` to chain multiple optimizations, be sure to execute a forward pass
after each call to `hybridize`.
+The `hybridize` function prepares the HybridBlock to be converted into a
backend symbol. The `backend` argument is a string that identifies which
backend that will partition the model. `**kwargs` are other user-specified
options (as a Python dictionary of strings mapped to strings) that will be
passed to the backend partitioning APIs. The `clear` argument defaults to
`False`, so it will chain optimizations together. If you want to clear clear
any previous optimizations done on the block, set `clear` to `True`. The actual
partitioning takes place during the forward pass. If you want to use
`hybridize` to chain multiple optimizations, be sure to execute a forward pass
after each call to `hybridize`.
If you just want to partition the HybridBlock but not run a complete forward
pass, you can use the `optimize_for` API that combines the work done in the
`hybridize` API with part of the work done in the forward pass.
```python
-block.optimize_for(x, backend=None, backend_opts=None, clear=True, **kwargs)
+block.optimize_for(x, backend=None, clear=True, **kwargs)
Review comment:
clear = False
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