// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail --dump-input-filter=all
// RUN: tf-tfrt-opt -pass-pipeline='builtin.module(tf-to-tfrt{target-tpurt=true tpu-use-core-selector=false})' %s | FileCheck %s --dump-input=fail --dump-input-filter=all

// CHECK-LABEL: func @_tfrt_fallback_init
// CHECK-SAME: {{.*}} !tfrt.chain
// CHECK: tfrt_fallback_async.createop(%arg0) key(0) device("/device:CPU:0") "tf.ParseExampleV2"()

 * to {@link #obtain()} is synchronized.
 * </p>
 *
 * <p>
 * A value source implementation is exempt from the automatic detection of work graph cache inputs.
 * For example, if the {@link #obtain()} method calls {@code System.getenv("FOO")} then changes to
 * the {@code FOO} environment variable only invalidate the cache if the value returned by the

  // CHECK-DAG: %[[INPUT:.*]] = mhlo.bitcast_convert %arg0 : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32:1, {2.000000e+00:4,2.000000e+00:4}>>
  // CHECK-DAG: %[[MAX:.*]] = mhlo.bitcast_convert %arg1 : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32:1, {2.000000e+00:4,2.000000e+00:4}>>
  // CHECK-DAG: %[[INPUT_1:.*]] = chlo.broadcast_maximum

                                PatternRewriter& rewriter) const override {
    auto input = op.getOptional();
    auto elementType = getElementTypeOrSelf(input.getType());
    if (isa<TF::VariantType>(elementType)) {
      // We can only replace OptionalGetValue after the inputs have been
      // replaced.
      return failure();
    }

  std::vector<tensorflow::Tensor> inputs;

  // Adds inputs to the TF operation.
  for (const ElementsAttr& operand : operands) {
    tensorflow::Tensor tensor;
    RETURN_FAILURE_IF_ERROR(tensorflow::ConvertToTensor(operand, &tensor));
    inputs.push_back(std::move(tensor));
  }

  std::vector<tensorflow::TensorValue> input_values;
  for (tensorflow::Tensor& tensor : inputs) {
    input_values.emplace_back();

#define GEN_PASS_DEF_ANNOTATEPARAMETERREPLICATIONPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

// Analyzes the inputs to ClusterFuncOps in the module, and annotates their
// invoked functions whether each input has the same data across replicas.
struct AnnotateParameterReplicationPass
    : public impl::AnnotateParameterReplicationPassBase<
          AnnotateParameterReplicationPass> {

      }))
    return failure();

  SmallVector<ElementsAttr, 4> inputs;
  inputs.reserve(operands.size());
  for (auto input : operands) {
    inputs.push_back(mlir::cast<ElementsAttr>(input));
  }

  SmallVector<Attribute> constants;
  LogicalResult status = EvaluateOperation(inst, inputs, constants);
  results.assign(constants.begin(), constants.end());
  return status;
}

                sources.set(file("inputDir"))
                outputFile.set(file("build/outputFile.txt"))
                ${ api == Api.RUNTIME_API
                    ? "inputs.dir(sources).withPathSensitivity(PathSensitivity.RELATIVE).withPropertyName('sources')"
                    : "" }
            }
        """
        def inputDir = file("inputDir").createDir()

// TensorFlow.
std::unique_ptr<OperationPass<ModuleOp>>
CreatePrepareTpuComputationForTfExportPass();

// Rewrites ops that require quantized inputs or outputs to ops that allow
// non-quantized inputs and outputs.
std::unique_ptr<OperationPass<func::FuncOp>> CreateLowerQuantizedPass();

// Reorders ops so ops of the same dialect are next to each other.

        val gradleUserHomeDir: File,
        val jvm: String,
        val startParameterProperties: Map<String, Any?>,
        /**
         * Whether the undeclared inputs accessed while serializing the task graph will be
         * excluded from input tracking. This is a temporary opt-out flag after a change
         * was made in that behavior.
         */
        val ignoreInputsInConfigurationCacheTaskGraphWriting: Boolean,