using mlir::func::FuncOp;
using tflite::OperatorT;
using tflite::TensorT;

namespace errors = tensorflow::errors;
namespace tfl = mlir::TFL;

namespace {

constexpr char kScatterRegionFuncName[] = "update_computation_func_name";

using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;
using ::tflite::IsValidBufferOffset;

// HWIO data format.
//
// Motivation to prepare for the TFLite legalization before the actual
// legalization is to exploit constant folding opportunities in any newly
// created ops by leveraging constant folding support for the TensorFlow ops.
// This way TFLite can be used as a serialization format only and does not
// require access to the TFLite runtime for optimizations as required by the
// TFLite team.

#include <climits>
#include <cstdint>

  flatbuffers::FlatBufferBuilder input_builder;
  tflite::FinishModelBuffer(input_builder,
                            tflite::Model::Pack(input_builder, model));

  const std::string input_buffer(
      reinterpret_cast<const char*>(input_builder.GetBufferPointer()),
      input_builder.GetSize());
  auto status = mlir::lite::QuantizeModel(
      input_buffer, input_type, output_type, inference_tensor_type,

// This is the optimization pattern definition file for TensorFlow Lite.

include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td"
include "tensorflow/compiler/mlir/lite/utils/utils.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

        "//tensorflow/lite/core:framework",
        "//tensorflow/lite/core/c:private_common",
        "//tensorflow/lite/delegates/flex:allowlisted_flex_ops_lib",
        "//tensorflow/lite/experimental/remat:metadata_util",
        "//tensorflow/lite/python/metrics:converter_error_data_proto_cc",
        "//tensorflow/lite/schema:schema_conversion_utils",
        "//tensorflow/lite/toco:toco_flags_proto_cc",

// This transformation pass prepares for legalization to the TFLite dialect by
// converting Tensorlist operations in TensorFlow dialect into operations that
// can be legalized to TensorFlow Lite dialect with simple replacements.  The
// newly created operations are in the TensorFlow dialect if the operation can
// be represented using a TensorFlow op. Otherwise, TensorFlow Lite dialect op
// is used.

#include <climits>
#include <cstdint>

#     rbe_linux_cuda_nvcc:            RBE options to build with GPU support using nvcc.
#
# Embedded Linux options (experimental and only tested with TFLite build yet)
#     elinux:          General Embedded Linux options shared by all flavors.
#     elinux_aarch64:  Embedded Linux options for aarch64 (ARM64) CPU support.

func ssaMarkMoves(s *ssagen.State, b *ssa.Block) {
	//	flive := b.FlagsLiveAtEnd
	//	if b.Control != nil && b.Control.Type.IsFlags() {
	//		flive = true
	//	}
	//	for i := len(b.Values) - 1; i >= 0; i-- {
	//		v := b.Values[i]
	//		if flive && (v.Op == v.Op == ssa.OpPPC64MOVDconst) {
	//			// The "mark" is any non-nil Aux value.
	//			v.Aux = v
	//		}
	//		if v.Type.IsFlags() {
	//			flive = false
	//		}
	//		for _, a := range v.Args {

// which is positioned at (tline, tcol) (only needed for error messages).
func (p *parser) updateBase(pos Pos, tline, tcol uint, text string) {
	i, n, ok := trailingDigits(text)
	if i == 0 {
		return // ignore (not a line directive)
	}
	// i > 0

	if !ok {
		// text has a suffix :xxx but xxx is not a number
		p.errorAt(p.posAt(tline, tcol+i), "invalid line number: "+text[i:])
		return
	}

    color: white;
  }
}
.button--blue {
  background: var(--gradle-blue);
  background: linear-gradient(var(--button-gradient-angle), var(--gradle-blue) 0%, var(--gradle-blue-lite) 100%);
  &:hover {
    background: var(--gradle-blue-lite);
  }
}

/* Custom Download button*/
/* CSS */
.button-9 {
	appearance: button;
	backface-visibility: hidden;
	background-color: #209BC4;