} else {
          return tflite::TensorType_INT4;
        }
      case 8:
        return itype.isUnsigned() ? tflite::TensorType_UINT8
                                  : tflite::TensorType_INT8;
      case 16:
        return itype.isUnsigned() ? tflite::TensorType_UINT16
                                  : tflite::TensorType_INT16;
      case 32:
        return itype.isUnsigned() ? tflite::TensorType_UINT32

  return op.getFeatureGroupCount() > 1;
}

// Returns kernel output feature dimension of TFLite convolutions.
int64_t GetConvolutionKernelOutputFeatureDimension(bool is_depthwise) {
  return is_depthwise ? 3 : 0;
}

// Returns kernel input feature dimension of TFLite convolutions.
int64_t GetConvolutionKernelInputFeatureDimension(bool is_depthwise) {
  return is_depthwise ? 0 : 3;
}

}  // namespace TFL
}  // namespace mlir

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_dialect.cc.inc"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_enums.cc.inc"
#define GET_ATTRDEF_CLASSES
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_attrdefs.cc.inc"
#define GET_OP_CLASSES
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.cc.inc"

namespace mlir {
namespace TFL {

  // Whether the TFLite operator has options in the schema representation.
  bit hasOptions = 0b0;
  // Whether the TFLite operator has options2 in the schema representation.
  bit hasOptions2 = 0b0;

  // Use to specify a custom options type for TFLite operators where
  // the option's name does not match the TFLite operator's name.

// CHECK{LITERAL}: dense<[1, 2]> : tensor<2xi32>
// CHECK: %[[DILATE:.+]] = "tfl.dilate"(%[[ARG0]], %[[INTERIOR]], %[[ARG1]]) : (tensor<2x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>, tensor<2xi32>, tensor<!quant.uniform<i8:f32, 2.000000e+00:-1>>) -> tensor<3x7x!quant.uniform<i8:f32, 2.000000e+00:-1>>

}

// -----

// CHECK-LABEL: testDilate
func.func @testDilate(%arg0: tensor<3x4x5xf32>) -> tensor<5x7x9xf32> {
  // CHECK: "tfl.dilate"(%arg0, %cst, %cst_0)
  %cst = arith.constant dense<1> : tensor<3xi32>
  %cst_0 = arith.constant dense<-1.0> : tensor<f32>
  %0 = "tfl.dilate"(%arg0, %cst, %cst_0) : (tensor<3x4x5xf32>, tensor<3xi32>, tensor<f32>) -> tensor<5x7x9xf32>
  func.return %0 : tensor<5x7x9xf32>

#include "stablehlo/dialect/ChloOps.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/hlo_matchers.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/reduce.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/util.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"

// expected-warning @+1 {{TFLite does not support batched input for non_max_suppression_padded}}

}

// A helper class to import a TensorFlow model expressed in SavedModel V1 into
// an MLIR Module in SavedModel dialect.
//
// TODO(b/179683149): Rename this class to avoid confusion with TFLite.
class SavedModelSignatureDefImporterLite {
 public:
  // Main entry point: converts all functions (specified by SignatureDefs) in
  // the given meta graph to an MLIR Module.
  //