}
}

const prolog = `

package main

import (
	"fmt"
	"os"
)

var (
	i8 int8 = 0x12
	i16 int16 = 0x1234
	i32 int32 = 0x12345678
	i64 int64 = 0x123456789abcdef0
	ui8 uint8 = 0x12
	ui16 uint16 = 0x1234
	ui32 uint32 = 0x12345678
	ui64 uint64 = 0x123456789abcdef0

	ni8 = ^i8
	ni16 = ^i16
	ni32 = ^i32
	ni64 = ^i64
	nui8 = ^ui8
	nui16 = ^ui16
	nui32 = ^ui32
	nui64 = ^ui64

  %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32>
  %1 = "tfl.quantize"(%0) {qtype = tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>) -> tensor<2x1xf32>

    graph will be converted into the following IR:

    %q_w = "tfl.pseudo_qconst"() {
         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %w = "tfl.dequantize"(%q_w) :
         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->
         tensor<64x3x3x3xf32>
    %conv = "tfl.conv_2d"(%input_act, %w, %bias)

    but if it is supported, it will be rewritten as:

    tf_executor.fetch
  }
  func.return
}

func.func @plain() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {type = i8} : () -> tensor<16xi8>
    tf_executor.fetch
  }
  func.return

	}
	I5 interface {
		m1(I5)
	}
	I6 interface {
		S0 /* ERROR "non-interface type S0" */
	}
	I7 interface {
		I1
		I1
	}
	I8 /* ERROR "invalid recursive type" */ interface {
		I8
	}
	I9 /* ERROR "invalid recursive type" */ interface {
		I10
	}
	I10 interface {
		I11
	}
	I11 interface {
		I9
	}

	C1 chan int
	C2 <-chan int

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    (TF_CastOp:$i8_cast
      (TF_ClipByValueOp:$clip $input, $min_value, $max_value),
      ConstBoolAttrFalse:$truncate2),
    ConstBoolAttrFalse:$truncate1),
  (TF_CastOp $clip, ConstBoolAttrFalse),
  [(TensorOf<[I8]> $i8_cast),
   (TensorOf<[I32]> $clip),
   (IsIntSplatValueEqual<"int32_t", "-128"> $min_value),
   (IsIntSplatValueEqual<"int32_t", "127"> $max_value)]>;

// This pattern optimizes:
//   (x + cst1) + cst2 -> x + cst

  }];
  let options = [
    Option<"unpack_quantized_types_", "unpack-quantized-types", "bool",
      /*default=*/"true", "Unpacks ops with uniform quantized types into "
      "operations without uniform quantized types (mostly i8 or i32).">
  ];
  let dependentDialects = [
    "mlir::stablehlo::StablehloDialect", "mlir::TF::TensorFlowDialect",
    "mlir::func::FuncDialect", "mlir::mhlo::MhloDialect",

// The TF dialect uses some TF types that are illegal in the MHLO dialect and
// some generic types that are legal in MHLO. This pass legalizes TF types into
// types that are legal in MHLO. For example, TF::Qint8Type is converted to i8.
// Rewrites here should run before TF to MHLO op legalizations are run.

#include <memory>

#include "absl/log/log.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/Casting.h"

//
namespace mlir {
namespace TFL {

namespace {

#define GEN_PASS_DEF_REDUCETYPEPRECISIONPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// This pattern checks if an i8 arith::ConstantOp tensor has all values within
// the INT4 range, i.e. [-8,7] and converts it into i4 if so. This assumes that
// the input is sign-extended two's complement.