#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "stablehlo/dialect/StablehloOps.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"

namespace mlir::quant::stablehlo {

#define GEN_PASS_DEF_POSTQUANTIZEPASS

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h"

{% endfor %}

</div>
{% endif %}

## Über diese Daten - technische Details

Der Hauptzweck dieser Seite ist es zu zeigen, wie die Gemeinschaft anderen hilft.

Das beinhaltet auch Hilfe, die normalerweise weniger sichtbar und in vielen Fällen mühsamer ist, wie, anderen bei Problemen zu helfen und Pull Requests mit Übersetzungen zu überprüfen.

yIsNan:			//  Pow(NaN, y) = NaN
ltZeroInt:		// Pow(x, y) = NaN for finite x < 0 and finite non-integer y
	MOVD	$NaN, R2
	MOVD	R2, ret+16(FP)
	RET
negOnePosInf:	// Pow(-1, ±Inf) = 1
negOneNegInf:
	MOVD	$PosOne, R3
	MOVD	R3, ret+16(FP)
	RET
negZeroOddInt:
	MOVD	$NegInf, R3
	MOVD	R3, ret+16(FP)
	RET
zeroNotOdd:		// Pow(±0, y) = +Inf for finite y < 0 and not an odd integer

     *
     * Consequently:
     * 1. If the previous mean is finite and the new value is non-finite then the new mean is that
     *    value (whether it is NaN or infinity).
     * 2. If the new value is finite and the previous mean is non-finite then the mean is unchanged
     *    (whether it is NaN or infinity).
     * 3. If both the previous mean and the new value are non-finite and...

    return 1;

  // Create TFLite interpreter & invoke converted program.
  std::unique_ptr<tflite::FlatBufferModel> model =
      tflite::FlatBufferModel::BuildFromBuffer(serialized_flatbuffer.c_str(),
                                               serialized_flatbuffer.size());
  tflite::ops::builtin::BuiltinOpResolver builtins;
  std::unique_ptr<tflite::Interpreter> interpreter;

#include "llvm/Support/raw_ostream.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include "tensorflow/lite/model.h"

using llvm::cl::opt;

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \
// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \
// RUN:   | FileCheck %s

     *
     * Consequently:
     * 1. If the previous mean is finite and the new value is non-finite then the new mean is that
     *    value (whether it is NaN or infinity).
     * 2. If the new value is finite and the previous mean is non-finite then the mean is unchanged
     *    (whether it is NaN or infinity).
     * 3. If both the previous mean and the new value are non-finite and...

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/convert_type.h"

        }
      }
    }

    os << "  tflite::" << tflite_option_name << "Builder b(*fbb);\n";
    for (const auto &option : options)
      os << formatv("  b.add_{0}(std::move({0}));\n", option);
    os << "  return b.Finish();\n}\n";
  }
}

// For each TFLite op, emits a builder function that packs the TFLite op into
// the corresponding FlatBuffer object.
//