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RELEASE.md
endpoint. * Promoting `tf.data.experimental.RandomDataset` API to `tf.data.Dataset.random` and deprecating the experimental endpoint. * Promoting `tf.data.experimental.scan` API to `tf.data.Dataset.scan` and deprecating the experimental endpoint. * Promoting `tf.data.experimental.snapshot` API to `tf.data.Dataset.shapshot` and deprecating the experimental endpoint.
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api/go1.13.txt
pkg crypto/x509, const PureEd25519 SignatureAlgorithm pkg database/sql, method (*Conn) Raw(func(interface{}) error) error pkg database/sql, method (*NullInt32) Scan(interface{}) error pkg database/sql, method (NullInt32) Value() (driver.Value, error) pkg database/sql, method (*NullTime) Scan(interface{}) error pkg database/sql, method (NullTime) Value() (driver.Value, error) pkg database/sql, type NullInt32 struct
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docs/en/docs/tutorial/index.md
│ │ │ <span style="background-color:#272822"><font color="#FF4689">from</font></span><span style="background-color:#272822"><font color="#F8F8F2"> main </font></span><span style="background-color:#272822"><font color="#FF4689">import</font></span><span style="background-color:#272822"><font color="#F8F8F2"> app</font></span><span style="background-color:#272822"> </span> │ │ │
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docs/en/docs/contributing.md
```console $ python ./scripts/docs.py live <span style="color: green;">[INFO]</span> Serving on http://127.0.0.1:8008 <span style="color: green;">[INFO]</span> Start watching changes <span style="color: green;">[INFO]</span> Start detecting changes ``` </div> It will serve the documentation on `http://127.0.0.1:8008`. That way, you can edit the documentation/source files and see the changes live.
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docs/en/docs/fastapi-cli.md
│ │ │ <span style="background-color:#272822"><font color="#FF4689">from</font></span><span style="background-color:#272822"><font color="#F8F8F2"> main </font></span><span style="background-color:#272822"><font color="#FF4689">import</font></span><span style="background-color:#272822"><font color="#F8F8F2"> app</font></span><span style="background-color:#272822"> </span> │ │ │
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docs/en/docs/tutorial/first-steps.md
│ │ │ <span style="background-color:#272822"><font color="#FF4689">from</font></span><span style="background-color:#272822"><font color="#F8F8F2"> main </font></span><span style="background-color:#272822"><font color="#FF4689">import</font></span><span style="background-color:#272822"><font color="#F8F8F2"> app</font></span><span style="background-color:#272822"> </span> │ │ │
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docs/en/docs/deployment/manually.md
│ │ │ <span style="background-color:#272822"><font color="#FF4689">from</font></span><span style="background-color:#272822"><font color="#F8F8F2"> main </font></span><span style="background-color:#272822"><font color="#FF4689">import</font></span><span style="background-color:#272822"><font color="#F8F8F2"> app</font></span><span style="background-color:#272822"> </span> │ │ │
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docs/en/docs/advanced/websockets.md
In your WebSocket route you can `await` for messages and send messages. ```Python hl_lines="48-52" {!../../../docs_src/websockets/tutorial001.py!} ``` You can receive and send binary, text, and JSON data. ## Try it If your file is named `main.py`, run your application with: <div class="termy"> ```console $ fastapi dev main.py
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tensorflow/c/eager/tape.h
Status ComputeGradient( const VSpace<Gradient, BackwardFunction, TapeTensor>& vspace, const absl::Span<const int64_t> target_tensor_ids, const absl::Span<const int64_t> source_tensor_ids, const std::unordered_map<int64, TapeTensor>& sources_that_are_targets, gtl::ArraySlice<Gradient*> output_gradients, absl::Span<Gradient*> result, bool build_default_zeros_grads = true);
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docs/en/docs/advanced/behind-a-proxy.md
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