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()

                .prepare()

        when:
        module.pom.expectGet()
        sourceArtifact.expectHead()
        sourceArtifact.expectGet()

        then:
        checkArtifactsResolvedAndCached()
    }

    def "resolve javadoc artifacts"() {
        fixture.requestingJavadoc()
                .expectJavadocArtifact("javadoc")
                .prepare()

        when:

                .prepare()

        when:
        module.ivy.expectGet()
        module.getArtifact(classifier: "my-sources").expectGet()

        then:
        checkArtifactsResolvedAndCached()
    }

    def "resolve javadoc artifacts"() {
        fixture.requestingJavadoc()
                .expectJavadocArtifact("my-javadoc")
                .prepare()

        when:

        buildFile << """
group = 'org.gradle'
version = '1.0'
repositories { ivy { url "${ivyRepo.uri}" } }
configurations { compile }
dependencies {
    compile "org.gradle:test:1.45"
}
"""
        resolve.prepare()

        when:
        succeeds "checkDeps"

        then:
        resolve.expectGraph {
            root(":", "org.gradle:test:1.0") {
                module("org.gradle:test:1.45") {