// RUN: tf-opt %s -tfl-prepare-quantize="quantize-allowlist=quantize_float_placeholder_only,not_reset_input" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="disable-set-input-nodes-quantization-params=true" | FileCheck --check-prefix=MixedPrecision %s
// RUN: tf-opt %s -tfl-prepare-quantize="is-qdq-conversion=true" | FileCheck --check-prefix=QDQ %s

// CHECK-LABEL: main
// Uses `main` function to match the default target function of QuantSpecs and

// RUN: tf-opt -tfl-prepare-tf %s | FileCheck %s
// RUN: tf-opt %s -tf-layout-optimization=force-data-format=NHWC -tfl-prepare-tf  | FileCheck --check-prefix=LAYOUT --dump-input=always %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true" -cse | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true legacy-float-scale=true" -cse| FileCheck --check-prefix=Legacy %s

// CHECK-LABEL: QuantizeLstmCellInput
func.func @QuantizeLstmCellInput(%arg0: tensor<1x28x28xf32>) -> tensor<1x28x20xf32> {
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

}

project(':tool') {
	dependencies {
		implementation project(':api')
		implementation project(':impl')
	}
}
"""

        def resolve = new ResolveTestFixture(buildFile)
        resolve.prepare()

        when:
        run("tool:checkDeps")

        then:
        resolve.expectGraph {
            root(":tool", "test:tool:") {
                project(":api", "test:api:") {

	// before PreBind, as if the some other PreBind plugin had failed.
	unreserveBeforePreBind *result
}

// prepare contains changes for objects in the API server.
// Those changes are applied before running the steps. This can
// be used to simulate concurrent changes by some other entities
// like a resource driver.
type prepare struct {
	filter     change
	prescore   change
	reserve    change
	unreserve  change

                return null;
            }
            return prepare();
        }

        @Override
        public Visitor getAll(Class<?> serviceType, ServiceProvider.Visitor visitor) {
            if (serviceType.isAssignableFrom(this.serviceClass)) {
                visitor.visit(prepare());
            }
            return visitor;
        }

        @Override

			if tc.expectBeforeWorker != nil {
				fns = append(fns, func() {
					expectPodSyncStatus(t, tc.expectBeforeWorker, podWorkers.w.podSyncStatuses[uid])
				})
			}

			if tc.prepare != nil {
				if fn := tc.prepare(t, podWorkers); fn != nil {
					fns = append(fns, fn)
				}
			}

			// set up an initial pod status for the UpdatePod invocation which is
			// reset before workers call the podCache

// used.  For example, Conv2D in TFLite which uses OHWI data format for filters
// is not supported in TensorFlow because TensorFlow requires filters in the
// HWIO data format.
//
// Motivation to prepare for the TFLite legalization before the actual
// legalization is to exploit constant folding opportunities in any newly
// created ops by leveraging constant folding support for the TensorFlow ops.

      while_op.getLoc(), body.getFunctionType().getResults(),
      FilterRange<Value, OperandRange>(while_op.getOperands(),
                                       resource_arg_uses),
      while_op->getAttrs());
  // Prepare for AddLoadsStoresOutsideControlFlowOp().
  llvm::SmallDenseMap<int64_t, std::pair<Type, int64_t>>
      arg_data_type_and_updated_output_index;
  for (const auto& entry : remaining_resource_data_types) {

    def resolve = new ResolveTestFixture(buildFile, "conf").expectDefaultConfiguration("runtime")

    def setup() {
        settingsFile << "rootProject.name='depsub'\n"
        resolve.prepare()
        resolve.addDefaultVariantDerivationStrategy()
    }

    void "forces multiple modules by rule"() {
        mavenRepo.module("org.utils", "impl", '1.3').dependsOn('org.utils', 'api', '1.3').publish()