.vscode

# This is where the result of the go build goes
/output*/
/_output*/
/_output

# Emacs save files
*~
\#*\#
.\#*

# Vim-related files
[._]*.s[a-w][a-z]
[._]s[a-w][a-z]
*.un~
Session.vim
.netrwhist

# cscope-related files
cscope.*

# Go test binaries
*.test
/hack/.test-cmd-auth

# JUnit test output from ginkgo e2e tests
/junit*.xml

# Mercurial files
**/.hg

     *
     * Rough instructions to regenerate the test outputs and verify correctness:
     * - Temporarily change this test:
     * --- Comment out assertEquals.
     * --- System.out.println(input + " " + simplifyPath(input));
     * --- fail(). (If the test were to pass, its output would be hidden.)
     * - Run the test.
     * - Pull the relevant lines of output from the test into a testcases file.
     * - Test the output:

     *
     * Rough instructions to regenerate the test outputs and verify correctness:
     * - Temporarily change this test:
     * --- Comment out assertEquals.
     * --- System.out.println(input + " " + simplifyPath(input));
     * --- fail(). (If the test were to pass, its output would be hidden.)
     * - Run the test.
     * - Pull the relevant lines of output from the test into a testcases file.
     * - Test the output:

    may start to fail. To fix this, either update any inference clients to send
    requests with the actual input and output keys used by the trainer code, or
    conversely, update the trainer code to name the input and output Tensors
    'inputs' and 'outputs', respectively. Signatures using the 'classify' and
    'regress' methods are not affected by this change; they will continue to

```

This will generate a TypeScript SDK in `./src/client`.

You can learn how to [install `@hey-api/openapi-ts`](https://heyapi.dev/openapi-ts/get-started) and read about the [generated output](https://heyapi.dev/openapi-ts/output) on their website.

### Using the SDK { #using-the-sdk }

Now you can import and use the client code. It could look like this, notice that you get autocompletion for the methods:

///

## Bir output modeli ekleyin { #add-an-output-model }

Bunun yerine, plaintext password içeren bir input modeli ve password’ü içermeyen bir output modeli oluşturabiliriz:

{* ../../docs_src/response_model/tutorial003_py310.py hl[9,11,16] *}

}
```

## Pydantic `bytes` for Output Data { #pydantic-bytes-for-output-data }

You can also use `bytes` fields with `ser_json_bytes` in the model config for output data, and Pydantic will *serialize* the bytes as base64 when generating the JSON response.

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

## Pydantic `bytes` for Input and Output Data { #pydantic-bytes-for-input-and-output-data }

      ListenableFuture<V> future) {
    if (future.isDone()) {
      return future;
    }
    NonCancellationPropagatingFuture<V> output = new NonCancellationPropagatingFuture<>(future);
    future.addListener(output, directExecutor());
    return output;
  }

  /** A wrapped future that does not propagate cancellation to its delegate. */

        "$@"

# Stop when "xargs" is not available.
if ! command -v xargs >/dev/null 2>&1
then
    die "xargs is not available"
fi

# Use "xargs" to parse quoted args.
#
# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
#
# In Bash we could simply go:
#
#   readarray ARGS < <( xargs -n1 <<<"$var" ) &&
#   set -- "${ARGS[@]}" "$@"
#

    }

    private Process createMockProcess(String output) {
        return new MockProcess(output);
    }

    private static class MockProcess extends Process {
        private final InputStream inputStream;

        public MockProcess(String output) {
            this.inputStream = new ByteArrayInputStream(output.getBytes());
        }

        @Override