samskalicky commented on a change in pull request #19543:
URL: https://github.com/apache/incubator-mxnet/pull/19543#discussion_r524856994
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File path: example/extensions/lib_subgraph/README.md
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@@ -102,27 +102,27 @@ Partitioning APIs in MXNet are available in both Symbol
and Gluon APIs. For the
sym.optimize_for(backend, args=None, aux=None, ctx=None, **kwargs)
```
-The `optimize_for` API takes at least 1 argument, `backend` which is a string
that identifies which backend to partition the model for. The `args` and `aux`
arguments are optional and take a list of NDArray or dict of str to NDArray.
They are used to infer shapes and types and before partitioning, and passed to
the backend to use during compilation. The `ctx` argument is optional and takes
a device context to infer storage types. It also takes any other user-specified
options that will be passed to the backend partitioning APIs.
+The `optimize_for` API takes at least 1 argument, `backend` which is a string
that identifies which backend to partition the model for. The `args` and `aux`
arguments are optional and take a list of NDArray or dict of str to NDArray.
They are used to infer shapes and types and before partitioning, and passed to
the backend to use during compilation. The `ctx` argument is optional and takes
a device context to infer storage types. It also takes any other user-specified
options that will be passed to the backend partitioning APIs. The backend
options can be passed as kwargs.
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)
Review comment:
we should remove all mentions of hybridize here
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