mseth10 commented on a change in pull request #19543:
URL: https://github.com/apache/incubator-mxnet/pull/19543#discussion_r525575877
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File path: example/extensions/lib_pass/README.md
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@@ -83,17 +84,7 @@ APIs in MXNet are available in both Symbol and Gluon APIs.
For the Symbol API, `
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 use to optimize the model. 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 executing the graph
pass. 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 APIs.
-
-For the Gluon API, `hybridize` can be called on HybridBlocks to execute a
graph pass on the internal CachedOp Symbol.
-
-```python
-block.hybridize(backend=None, backend_opts=None, **kwargs)
-```
-
-The `hybridize` function prepares the HybridBlock to be converted into a
backend symbol. The `backend` argument is a string that identifies which pass
that will be executed on the model. The `backend_opts` takes other
user-specified options that will be passed to the backend APIs. The actual pass
runs once just before the first the forward pass.
-
-If you just want to run a graph pass on 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.
+The `optimize_for` API takes at least 1 argument, `backend` which is a string
that identifies which backend to use to optimize the model. 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 executing the graph
pass. 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 APIs (in the `kwargs`).
Review comment:
There are two different APIs. The symbol optimize_for API needs only
backend. The block optimize_for API needs only x. Here we are referring to the
first one.
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