if len(endpoints) == 0 || setCount == 0 || setDriveCount == 0 {
		return nil, nil, errInvalidArgument
	}

	// prepare getElapsedTime() to calculate elapsed time since we started trying formatting disks.
	// All times are rounded to avoid showing milli, micro and nano seconds
	formatStartTime := time.Now().Round(time.Second)

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true disable-per-channel=true" | FileCheck --check-prefix=PerTensor %s

// CHECK-LABEL: uint8_to_int8
func.func @uint8_to_int8(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>} : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-dynamic-range-per-channel-quantization=false" | FileCheck --check-prefix=PerTensor %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-float16-quantization" | FileCheck --check-prefix=Float16 %s

// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf | FileCheck --dump-input=always %s
// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf=use-fake-quant-num-bits=true | FileCheck --check-prefix LOBIT --dump-input=always %s

// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf | FileCheck --dump-input=always %s
// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf=use-fake-quant-num-bits=true | FileCheck --check-prefix LOBIT --dump-input=always %s

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true activation-number-of-bits=16" -cse | FileCheck %s

// CHECK-LABEL: QuantizeUnidirectionalLstmFullPerTensor
func.func @QuantizeUnidirectionalLstmFullPerTensor(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) {
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>

			prepare: func(mock *fakeImpl) {
				mock.NewRemoteImageServiceReturns(nil, errTest)
			},
			shouldError: true,
		},
	} {
		t.Run(tc.name, func(t *testing.T) {
			containerRuntime := NewContainerRuntime("")
			mock := &fakeImpl{}
			if tc.prepare != nil {
				tc.prepare(mock)
			}
			containerRuntime.SetImpl(mock)

			err := containerRuntime.Connect()

    // region Graph Variant failures
    def "demonstrate ambiguous graph variant selection failure with single disambiguating value for project"() {
        ambiguousGraphVariantForProjectWithSingleDisambiguatingAttribute.prepare()

        expect:
        assertResolutionFailsAsExpected(ambiguousGraphVariantForProjectWithSingleDisambiguatingAttribute)

        and: "Has error output"

//===----------------------------------------------------------------------===//
// The prepare-quantize-drq Pass.
//
namespace mlir {
namespace quant {

namespace {

using QuantizationUnit = std::pair<Operation*, int>;
using QuantizationUnits = llvm::SetVector<QuantizationUnit>;
using ::tensorflow::quantization::OpSet;

// Applies prepare quantization on the model in TF dialect for dynamic range
// quantization case.

        when(logger.isInfoEnabled()).thenReturn(true);
        executionEventLogger = new ExecutionEventLogger(messageBuilderFactory, logger);
    }

    @Test
    void testProjectStarted() {
        // prepare
        File basedir = new File("").getAbsoluteFile();
        ExecutionEvent event = mock(ExecutionEvent.class);
        MavenProject project = mock(MavenProject.class);