}
    }

    private
    fun extractArtifactName(fileName: String): String {
        return jarArtifactRegex.matchEntire(fileName)
            ?.groupValues
            ?.get(1)
            ?: error("Cannot derive artifact name from: $fileName")
    }

    private
    fun minify(artifact: File, keepClasses: Set<String>, jarFile: File): File {
        val tempDirectory = java.nio.file.Files.createTempDirectory(jarFile.name).toFile()

        configureEach {
            // Everything compiles to Java 17 by default
            targetJvmVersion = 17
        }
    }
    addCompilationFrom(sourceSets.main) {
        // For the production code, we derive the JVM version from the target runtime
        targetJvmVersion = gradleModule.targetRuntimes.computeProductionJvmTargetVersion()
    }
    addCompilationFrom(sourceSets.test)
}

removeTeamcityTempProperty()

    }
  }

  /**
   * Creates an {@code ImmutableMap<String, String>} from a {@code Properties} instance. Properties
   * normally derive from {@code Map<Object, Object>}, but they typically contain strings, which is
   * awkward. This method lets you get a plain-old-{@code Map} out of a {@code Properties}.
   *

</pre>

<p>
yields a function value representing <code>Mp</code> with signature
</p>

<pre>
func(tp *T, f float32) float32
</pre>

<p>
For a method with a value receiver, one can derive a function
with an explicit pointer receiver, so
</p>

<pre>
(*T).Mv
</pre>

<p>
yields a function value representing <code>Mv</code> with signature
</p>

<pre>

So, you could add additional data to the automatically generated schema.

For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.

You could do that with `openapi_extra`:

common:mkl --define=build_with_mkl=true --define=enable_mkl=true
common:mkl --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl --define=build_with_openmp=true
common:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

};

TEST_F(CApiFunctionTest, OneOp_ZeroInputs_OneOutput) {
  /*
   *                constant
   *                   |
   *                   v
   */
  // Define
  TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10");
  Define(-1, {}, {}, {c}, {});

  // Use, run, and verify
  TF_Operation* func_op = Use({});
  Run({}, func_op, 10);
  VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}},

counts the number of HTTP transports that the program
has configured without HTTP/2 support.

## Default GODEBUG Values {#default}

When a GODEBUG setting is not listed in the environment variable,
its value is derived from three sources:
the defaults for the Go toolchain used to build the program,
amended to match the Go version listed in `go.mod`,
and then overridden by explicit `//go:debug` lines in the program.

H3H V25 #define XH3 V26 #define XM3 V27 #define IN3 V28 #define H4L V29 #define H4 V30 #define H4H V31 #define IN0 IN #define H21L HL #define H21H HH #define LOPERM H2L #define HIPERM H2H #define VXL VS32 #define VIN VS35 #define VXC2 VS40 #define VH VS41 #define VHH VS42 #define VHL VS43 #define VIN1 VS48 #define VH2 VS49 #define VH2H VS50 #define VH2L VS51 #define VIN2 VS54 #define VH3L VS55 #define VH3 VS56 #define VH3H VS57 #define VIN3 VS60 #define VH4L VS61 #define VH4 VS62 #define VH4H VS63...

This is normally called a **webhook**.

## Webhooks steps { #webhooks-steps }

The process normally is that **you define** in your code what is the message that you will send, the **body of the request**.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.