// RUN: tac-opt-all-backends -tfl-device-transform-gpu %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> {
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return %0 : tensor<2x1xf32>
}

// CHECK:   func @pack(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {

        ListMultimap<K, V1> fromMultimap, EntryTransformer<? super K, ? super V1, V2> transformer) {
      super(fromMultimap, transformer);
    }

    @Override
    List<V2> transform(@ParametricNullness K key, Collection<V1> values) {
      return Lists.transform((List<V1>) values, Maps.asValueToValueFunction(transformer, key));
    }

    @Override
    public List<V2> get(@ParametricNullness K key) {

            destination[i] = transformer.transform(list[i]);
        }
        return destination;
    }

    public static <R, I> List<R> collect(I[] list, InternalTransformer<? extends R, ? super I> transformer) {
        return collect(Arrays.asList(list), transformer);
    }

    public static <R, I> Set<R> collect(Set<? extends I> set, InternalTransformer<? extends R, ? super I> transformer) {

  unsigned arg_num;
  unsigned num_users;
};

#define GEN_PASS_DEF_TPUSPACETODEPTHPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

// TODO(wangtao): add a pass to check if it is profitable to space to depth
// transform and invoke the transform if it is needed.
struct TPUSpaceToDepthPass
    : public impl::TPUSpaceToDepthPassBase<TPUSpaceToDepthPass> {
  void runOnOperation() override;
};

        when:
        runBuild("resolve", EnumSet.complementOf(EnumSet.of(OperationType.TRANSFORM)))

        then:
        events.operations.findAll { it.transform }.empty
    }

    def "reports dependencies of transform operations"() {
        given:
        buildFile << """
            $fileSizer
            $fileNamer
            project(":app") {

                        return project.getConfigurations().create("transformed", transformed -> {
                            transformed.setCanBeConsumed(false);
                            transformed.setCanBeResolved(true);
                            transformed.extendsFrom(base);

                            transformed.getAttributes().attribute(ARTIFACT_TYPE_ATTRIBUTE, TXT_TYPE);
                        });

            if (!transform.requiresInputChanges()) {
                // Non-incremental project artifact transforms also run in an immutable workspace
                identityCache = immutableWorkspaceServices.getIdentityCache();
                execution = new NormalizedIdentityImmutableTransformExecution(
                    transform,
                    inputArtifact,

package org.gradle.internal.scripts;

import org.gradle.api.internal.file.FileCollectionFactory;
import org.gradle.internal.classpath.transforms.ClasspathElementTransform;
import org.gradle.internal.classpath.transforms.ClasspathElementTransformFactoryForLegacy;
import org.gradle.internal.classpath.transforms.InstrumentingClassTransform;
import org.gradle.internal.classpath.types.InstrumentationTypeRegistry;

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"

namespace mlir {
namespace TF {

namespace {

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/RegionUtils.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/op_or_arg_name_mapper.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"