bool post_training_quantization = false;

  // Whether to allow dynamic range quantization. This is the easiest
  // quantization mode which doesn't require QAT or sample inputs.
  // This option only targets `DT_HALF` and `DT_QINT8` inference type.
  bool weight_quantization = false;

  // Whether to use the MLIR dynamic range quantizer instead of TOCO.
  bool enable_mlir_dynamic_range_quantizer = false;

	for _, v := range ns.set {
		m = append(m, v)
	}
	return m
}

// Option sets options for the template. Options are described by
// strings, either a simple string or "key=value". There can be at
// most one equals sign in an option string. If the option string
// is unrecognized or otherwise invalid, Option panics.
//
// Known options:
//
// missingkey: Control the behavior during execution if a map is

        new RuntimeException('Something went wrong', new GradleException('Unknown command line option'))                              | 'Unknown command line option' | 'exception having single parent cause'
        new RuntimeException('Something went wrong', new GradleException('Unknown command line option', new Exception('Total fail'))) | 'Total fail'                  | 'exception having multiple parent causes'
    }

  }
  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Preprocess TF op prior to quantization";
  }

  void runOnOperation() override;

 private:
  Option<OpSet> op_set_{
      *this, "target-opset", llvm::cl::init(OpSet::UNIFORM_QUANTIZED),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",

    }

    /**
     * Returns options for running the Groovy compiler in a separate process. These options only take effect
     * if {@code fork} is set to {@code true}.
     */
    @Nested
    public GroovyForkOptions getForkOptions() {
        return forkOptions;
    }

    /**
     * Sets options for running the Groovy compiler in a separate process. These options only take effect

  }
  return kTrSuccess;
}

int main(int argc, char **argv) {
  // TODO(jpienaar): Revise the command line option parsing here.
  tensorflow::InitMlir y(&argc, &argv);

  // TODO(antiagainst): We are pulling in multiple transformations as follows.
  // Each transformation has its own set of command-line options; options of one
  // transformation can essentially be aliases to another. For example, the

			continue field
		}

		if key == "all" {
			for i := range options {
				options[i].Specified = true
				options[i].Enable = enable || options[i].Required
			}
			continue field
		}

		for i := range options {
			if options[i].Name == key {
				options[i].Specified = true
				options[i].Enable = enable
				continue field
			}

    assertFalse(out.closed());
  }

  public void testClosesOnErrors_copyingToByteSinkThatThrows() {
    for (TestOption option : EnumSet.of(OPEN_THROWS, WRITE_THROWS, CLOSE_THROWS)) {
      TestByteSource okSource = new TestByteSource(bytes);
      assertThrows(IOException.class, () -> okSource.copyTo(new TestByteSink(option)));
      // ensure stream was closed IF it was opened (depends on implementation whether or not it's

        then:
        rootProjectDslFixtureFor(BuildInitDsl.GROOVY).assertGradleFilesGenerated()
    }

    def "prompts to overwrite files if any exist and does not creates gradle files for no option"() {
        given: "a file exists in the build directory"
        targetDir.file(defaultFileName).touch()

        when:
        def handle = startInteractiveExecutorWithTasks(
            "init",

)

// RestoreCountersFlag is an option flag for Restore
type RestoreCountersFlag bool

// RestoreCounters a boolean true constant for the option flag RestoreCountersFlag
const RestoreCounters RestoreCountersFlag = true

// NoRestoreCounters a boolean false constant for the option flag RestoreCountersFlag
const NoRestoreCounters RestoreCountersFlag = false

// FlushFlag an option flag for Flush
type FlushFlag bool