%1 = "tf.MultiDeviceIteratorGetNextFromShard"(%0, %cst, %arg1) {device = ""} : (tensor<!tf_type.resource>, tensor<i32>, tensor<i64>) -> tensor<!tf_type.string>
  %2 = "tf.Identity"(%1) {device = ""} : (tensor<!tf_type.string>) -> tensor<!tf_type.string>
  return %2 : tensor<!tf_type.string>
}

    ```mlir
    func.func @single_state_single_dataset_type_no_arguments(%arg0: tensor<!tf_type.variant>, %arg1: tensor<i64>) {
      %0 = "tf.AnonymousIteratorV3"() {output_shapes = [#tf_type.shape<32>], output_types = [f32]} : () -> tensor<!tf_type.resource>
      "tf.MakeIterator"(%arg0, %0) : (tensor<!tf_type.variant>, tensor<!tf_type.resource>) -> ()
      %cst = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>

      // Results in a bad mismatch of shapes.
      %updated_list = "tf.TensorListSetItem"(%list, %index, %element) : (tensor<!tf_type.variant<tensor<64x1xbf16>>>, tensor<i32>, tensor<64x1xbf16>) -> tensor<!tf_type.variant<tensor<64x1xbf16>>>

      return %updated_list : tensor<!tf_type.variant<tensor<64x1xbf16>>>
    }
  })";

  auto compilation_result = CompileMlirModule(
      kTensorListSetItemDimensionTooLarge,

/// offset applied to generate the range values. The output tensor needs to
/// have a static shape.
///
/// For example an op like the following:
///   %result = "tf.Range"(%start, %limit, %delta) {Tidx = "tfdtype$DT_FLOAT"}
///      : (tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<5xf32>
///
/// Output would be:
///   %iota = "mhlo.iota"() {iota_dimension = 0 : i64} : () -> tensor<5xf32>

                  mlir::tf_saved_model::TensorFlowSavedModelDialect,
                  mlir::func::FuncDialect, mlir::shape::ShapeDialect,
                  mlir::arith::ArithDialect, mlir::tf_type::TFTypeDialect,
                  mlir::quant::QuantizationDialect, mlir::tensor::TensorDialect,
                  mlir::quantfork::QuantizationForkDialect,
                  mlir::stablehlo::StablehloDialect,

#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 {

#define GEN_PASS_DEF_DECOMPOSEOPTIONALSPASS

// This method will actually merge the information contained in the
// types, it is capable of refining:
//   tensor<!tf_type.variant<tensor<?x8xf32>>>
// and:
//   tensor<!tf_type.variant<tensor<10x?xf32>>>
// into:
//   tensor<!tf_type.variant<tensor<10x8xf32>>>
//
// In case of inconsistencies (rank disagreement for example), it returns `lhs`.
Type TypeMeet(Type lhs, Type rhs) {

#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_structs.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/device_util.h"
#include "tensorflow/compiler/mlir/utils/string_container_utils.h"
#include "xla/array4d.h"
#include "xla/service/computation_placer.h"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/translate/export_tf_dialect_op.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"