y = math_ops.add(x, z)
  array_ops.identity(y, name='result')


def tftop_k(_):
  x = array_ops.placeholder(dtypes.int32, shape=[5], name='x')
  output = nn_ops.top_k(x, 2, name='values')
  array_ops.identity(output[1], name='indices')


def tfvariable_readonly(_):
  x = variables.Variable(1000.0, name='x')
  unused_y = variables.Variable(1000.0, name='y')
  old_x = x.value()

    seedA += part1;
    seedB = rotateRight(seedB + seedA + part4, 21);
    long c = seedA;
    seedA += part2;
    seedA += part3;
    seedB += rotateRight(seedA, 44);
    output[0] = seedA + part4;
    output[1] = seedB + c;
  }

  private static long hashLength0to16(byte[] bytes, int offset, int length) {
    if (length >= 8) {
      long mul = K2 + length * 2L;

    seedA += part1;
    seedB = rotateRight(seedB + seedA + part4, 21);
    long c = seedA;
    seedA += part2;
    seedA += part3;
    seedB += rotateRight(seedA, 44);
    output[0] = seedA + part4;
    output[1] = seedB + c;
  }

  private static long hashLength0to16(byte[] bytes, int offset, int length) {
    if (length >= 8) {
      long mul = K2 + length * 2L;

            PublishArtifact artifact = artifacts[0]
            assert artifact.name == 'toto'
        }
        1 * variant.setDescription(_)
        _ * sourceSet.getOutput() >> output
        1 * output.getClassesDirs() >> classes
        1 * sourceSet.getName()
        0 * _
    }

    def "configures attributes"() {
        def attrs = AttributeTestUtil.attributesFactory().mutable()

                      }
                    }));

  string constructor_call_str = absl::Substitute(
      "    tensorflow::ops::$0(scope.WithOpName(\"output\")$1);\n",
      op_info.op_name,
      strings::StrCat(
          op_info.api_def.arg_order().empty()
              ? absl::StrJoin(op_info.api_def.in_arg(), "",

        """
        buildFile.text = """
            task t
        """

        when:
        succeeds "t", "--scan"

        then:
        fixture.issuedNoPluginWarningCount(output, 1)
    }

    def "does not auto-apply plugin when Gradle Enterprise plugin is applied using plugin ID"() {
        when:
        gradleEnterpriseFixture.publishDummyPlugin(executer)

	})
	fmt.Println(result)
	// Output:
	// 1
}

func ExampleContainsFunc() {
	numbers := []int{0, 42, -10, 8}
	hasNegative := slices.ContainsFunc(numbers, func(n int) bool {
		return n < 0
	})
	fmt.Println("Has a negative:", hasNegative)
	hasOdd := slices.ContainsFunc(numbers, func(n int) bool {
		return n%2 != 0
	})
	fmt.Println("Has an odd number:", hasOdd)
	// Output:
	// Has a negative: true

	input8, output8 = output8, input8
	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)
		input64[i] = u64(in)

    %cst = stablehlo.constant dense<0.000000e+00>: tensor<10x1024x3xf32>

  %graph_result = tf_executor.graph {
    %output0, %control0 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a : tensor<i32>
    }
    %output1, %control1 = tf_executor.island {
      %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %b : tensor<i32>
    }
    tf_executor.fetch %output1 : tensor<i32>
  }