]</arg>
			<arg>pdfExtractor</arg>
		</postConstruct>
		<postConstruct name="addExtractor">
			<arg>[
				"application/x-lha",
				"application/x-lharc"
				]</arg>
			<arg>lhaExtractor</arg>
		</postConstruct>
		<postConstruct name="addExtractor">
			<arg>[
				"message/rfc822"
				]</arg>
			<arg>emlExtractor</arg>
		</postConstruct>
		<postConstruct name="addExtractor">
			<arg>[

    for (const OpDef::ArgDef& arg : fdef.signature().output_arg()) {
      const auto& iter = fdef.ret().find(arg.name());
      if (iter != fdef.ret().end()) {
        const auto& v = a_edges.insert({iter->second, arg.name()});
        ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();
      } else {

getHasArgument(); public abstract CommandLineParser$ParserState onComplete(); } org/apache/maven/wrapper/cli/CommandLineParser$OptionString.class package org.apache.maven.wrapper.cli; synchronized class CommandLineParser$OptionString { private final String arg; private final String option; private void CommandLineParser$OptionString(String, String); public String getDisplayName(); public String toString(); } org/apache/maven/wrapper/cli/CommandLineParser$OptionStringComparat.class package org.apache.maven.wrapper.cli;...

		if f == nil && fb == nil {
			continue
		}
		var actual string
		if f != nil {
			actual = string(f([]byte(tc.in), tc.arg))
		} else {
			actual = string(fb([]byte(tc.in), []byte(tc.arg)))
		}
		if actual != tc.out {
			t.Errorf("%s(%q, %q) = %q; want %q", name, tc.in, tc.arg, actual, tc.out)
		}
	}

	for _, tc := range trimNilTests {
		name := tc.f
		f, fb := toFn(name)

  EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
  EXPECT_EQ(string("cancel"), TF_Message(s));
  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

    assertEquals(reserialized, substitute(reserialized, String.class));
  }

  private static <T, X> TypeToken<T> substitute(TypeToken<T> type, Class<X> arg) {
    return type.where(new TypeParameter<X>() {}, arg);
  }

  private abstract static class ToReproduceGenericSignatureFormatError<V> {
    private abstract class BaseOuter {
      abstract class BaseInner {}
    }

    assertEquals(reserialized, substitute(reserialized, String.class));
  }

  private static <T, X> TypeToken<T> substitute(TypeToken<T> type, Class<X> arg) {
    return type.where(new TypeParameter<X>() {}, arg);
  }

  private abstract static class ToReproduceGenericSignatureFormatError<V> {
    private abstract class BaseOuter {
      abstract class BaseInner {}
    }

notify_elasticsearch  publish bucket notifications to Elasticsearch endpoints
notify_redis          publish bucket notifications to Redis datastores
```

> NOTE:
>
> - '\*' at the end of arg means its mandatory.
> - '\*' at the end of the values, means its the default value for the arg.
> - When configured using environment variables, the `:name` can be specified using this format `MINIO_NOTIFY_WEBHOOK_ENABLE_<name>`.

## Publish MinIO events via AMQP

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//   are not optimizing for convenience.
// * We make assumption that one session has one graph.  This should be
//   fine since we have the ability to run sub-graphs.
// * We could allow NULL for some arguments (e.g., NULL options arg).
//   However since convenience is not a primary goal, we don't do this.
// * Devices are not in this API.  Instead, they are created/used internally
//   and the API just provides high level controls over the number of