let constructor = "mlir::odml::CreateUniformQuantizedStableHloToTflPass()";
  let description = [{
    Converts StableHLO ops that accept or return uniform quantized types to
    equivalent ops in the TFLite dialect.
  }];
  let dependentDialects = [
    "stablehlo::StablehloDialect",
    "quant::QuantizationDialect",
    "mlir::TFL::TFLDialect",
  ];
}

  /**
   * Helper class for testing with non-finite values. {@link #ALL_MANY_VALUES} gives a number of
   * instances with many combinations of finite and non-finite values. All have {@link
   * #MANY_VALUES_COUNT} values. If all the values are finite then the mean is {@link
   * #MANY_VALUES_MEAN} and the sum-of-squares-of-deltas is {@link
   * #MANY_VALUES_SUM_OF_SQUARES_OF_DELTAS}. The smallest and largest finite values are always

// There are four parts: a boolean negative flag, a form byte with three possible states
// (finite=0, infinite=1, NaN=2), a base-2 big-endian integer
// coefficient (also known as a significand) as a []byte, and an int32 exponent.
// These are composed into a final value as "decimal = (neg) (form=finite) coefficient * 10 ^ exponent".
// A zero length coefficient is a zero value.

// RUN: not flatbuffer_translate -mlir-to-tflite-flatbuffer %s 2>&1 | FileCheck %s

// CHECK: error: 'tf.MyCustomOp' op is neither a custom op nor a flex op
// CHECK: error: failed while converting: 'main'
// CHECK: Some ops in the model are custom ops, See instructions to implement
// CHECK: tf.MyCustomOp(tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>, tensor<3xf32>) : {name = "MyCustomOp"}

func.func @main(tensor<4xf32>) -> tensor<4xf32> {

 * The representation of a linear transformation between real numbers {@code x} and {@code y}.
 * Graphically, this is the specification of a straight line on a plane. The transformation can be
 * expressed as {@code y = m * x + c} for finite {@code m} and {@code c}, unless it is a vertical
 * transformation in which case {@code x} has a constant value for all {@code y}. In the
 * non-vertical case, {@code m} is the slope of the transformation (and a horizontal transformation

// Version 2: Rename operators to conform to NN API.
// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.

namespace tflite;

// This corresponds to the version.
file_identifier "TFL3";
// File extension of any written files.
file_extension "tflite";

// IMPORTANT: All new members of tables, enums and unions must be added at the
// end to ensure backwards compatibility.

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s


module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>

   *   computeInPlace;
   * - (except that, for non-finite values, we don't do all combinations exhaustively);
   * - percentiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace.
   */

  private static final double ALLOWED_ERROR = 1.0e-10;

  /**
   * A {@link Correspondence} which accepts finite values within {@link #ALLOWED_ERROR} of each
   * other.
   */

      // If no main is specified in the allowlist, use the 1st func
      // in trim_funcs_allowlist as the main.
      // TODO(ashwinm): Currently tflite flatbuffer export assumes there is
      // always a main. This is strictly not required for TFlite. We need to
      // remove that restriction once we have support to attribute the main
      // tensorflow function in MLIR TF import using an entry_point attr.

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"

namespace mlir {
namespace lite {

// Sparsify the `input_model` and write the result to a flatbuffer `builder`.
absl::Status SparsifyModel(const tflite::ModelT& input_model,
                           flatbuffers::FlatBufferBuilder* builder);
}  // namespace lite
}  // namespace mlir