* Base64 encode user info for HTTP Basic Authentication.
     *
     * Try to use {@literal java.util.Base64} encoder which is available starting with Java 8.
     * Fallback to {@literal javax.xml.bind.DatatypeConverter} from JAXB which is available starting with Java 6 but is not anymore in Java 9.
     * Fortunately, both of these two Base64 encoders implement the right Base64 flavor, the one that does not split the output in multiple lines.
     *

                "jackson-annotations-2.4.0.jar", "jackson-core-2.4.3.jar", "jackson-databind-2.4.3.jar", "jackson-module-jaxb-annotations-2.4.3.jar",
                "publishTest-1.9.jar", "spring-core-2.5.6.jar"
        }
    }

    def "can publish java-library with strict dependencies"() {
        requiresExternalDependencies = true

				<exclusion>
					<groupId>javax.activation</groupId>
					<artifactId>javax.activation-api</artifactId>
				</exclusion>
				<exclusion>
					<groupId>javax.xml.bind</groupId>
					<artifactId>jaxb-api</artifactId>
				</exclusion>
				<exclusion>
					<groupId>jakarta.annotation</groupId>
					<artifactId>jakarta.annotation-api</artifactId>
				</exclusion>
				<exclusion>

	fmt.Printf("%q\n", re.FindAllStringSubmatch("-ab-axb-", -1))
	fmt.Printf("%q\n", re.FindAllStringSubmatch("-axxb-ab-", -1))
	// Output:
	// [["ab" ""]]
	// [["axxb" "xx"]]
	// [["ab" ""] ["axb" "x"]]
	// [["axxb" "xx"] ["ab" ""]]
}

func ExampleRegexp_FindAllStringSubmatchIndex() {
	re := regexp.MustCompile(`a(x*)b`)
	// Indices:
	//    01234567   012345678
	//    -ab-axb-   -axxb-ab-

    %2 = "tf.XlaCallModule"(%arg0, %0) <{Sout = [#tf_type.shape<1x3>], module = "", version = 9 : i64}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1", _stablehlo_module_attrs = {jax.uses_shape_polymorphism = true}, _tfl_quant_trait = "fully_quantizable"} : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %3 = stablehlo.concatenate %2, %1, dim = 0 : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<2x3xf32>

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =

    %9 = "quantfork.qcast"(%8) {volatile} : (tensor<2x3x1x3xf32>) -> tensor<2x3x1x3x...

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \