} 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.

Paper be removed farther from the Prism, and the several Ranges of Colours will be dilated and expanded into one another more and more, and by mixing their Colours will dilute one another, and at length, when the distance of the Paper from the Comb is about a Foot, or a little more (suppose in the Place 2D 2E) they will so far dilute one another, as to become white. With any Obstacle, let all the Light be now stopp'd which passes through any one Interval of the Teeth, so that the Range of Colours which...

// 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>>

(){var i=this,n=this.getValidIndex();this.navItems.forEach(function(t){var e=ht(t,i.attrItem);He(t,i.clsActive,F(e)===n),He(t,"uk-invisible",i.finite&&("previous"===e&&0===n||"next"===e&&n>=i.maxIndex))})}}}],props:{clsActivated:Boolean,easing:String,index:Number,finite:Boolean,velocity:Number,selSlides:String},data:function(){return{easing:"ease",finite:!1,velocity:1,index:0,prevIndex:-1,stack:[],percent:0,clsActive:"uk-active",clsActivated:!1,Transitioner:!1,transitionOptions:{}}},connected:fu...

}

// -----

// 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}}