### Testé

* 100% <abbr title="La quantité de code qui est testé automatiquement">de couverture de test</abbr>.

        "passes/merge_fusion_with_dequantize.cc",
        "passes/nchw_convolution_to_nhwc.cc",
        "passes/optimize_graph.cc",
        "passes/post_quantize.cc",
        "passes/prepare_quantize.cc",
        "passes/quantize.cc",
        "passes/quantize_composite_functions.cc",
        "passes/quantize_weight.cc",
        "passes/remove_sharding_custom_call.cc",
        "passes/remove_sharding_custom_call.inc",

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

// Tests if reduce_window op following quantized function is quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1722 : i32}, tf_saved_model.semantics} {
  // CHECK-LABEL: main_00
  // CHECK-SAME: %[[ARG0:.*]]: tensor<2x3x1x1024xf32>

		// might not start at (0, 0).
		//
		// TODO: Pick a better sub-sample of the Plan 9 palette.
		pm = image.NewPaletted(b, palette.Plan9[:opts.NumColors])
		if opts.Quantizer != nil {
			pm.Palette = opts.Quantizer.Quantize(make(color.Palette, 0, opts.NumColors), m)
		}
		opts.Drawer.Draw(pm, b, m, b.Min)
	}

	// When calling Encode instead of EncodeAll, the single-frame image is

    const mlir::TFL::PassConfig& pass_config,
    mlir::OpPassManager* pass_manager) {
  // This pass wraps all the tf.FakeQuant ops in a custom op so they are not
  // folded before being converted to tfl.quantize and tfl.dequantize ops.
  auto wrapped_ops = mlir::TFL::AllTfFakeQuantOps();
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateRaiseCustomOpsPass(wrapped_ops));

	Set(x, y int, c color.Color)
	SetRGBA64(x, y int, c color.RGBA64)
}

// Quantizer produces a palette for an image.
type Quantizer interface {
	// Quantize appends up to cap(p) - len(p) colors to p and returns the
	// updated palette suitable for converting m to a paletted image.
	Quantize(p color.Palette, m image.Image) color.Palette
}

// Op is a Porter-Duff compositing operator.
type Op int

const (

  ):
    # Check and populate calibration options.
    _populate_calibration_options(quantization_options)


@tf_export.tf_export('quantization.experimental.quantize_saved_model')
def quantize(
    saved_model_path: str,
    output_directory: Optional[str] = None,
    quantization_options: Optional[_QuantizationOptions] = None,
    representative_dataset: Optional[

  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,
  NON_MAX_SUPPRESSION_V5 = 121,
  SCATTER_ND = 122,
  SELECT_V2 = 123,

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: same_scale_after_composite
  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  func.func private @same_scale_after_composite(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<3x1xf32> {

  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,
  NON_MAX_SUPPRESSION_V5 = 121,
  SCATTER_ND = 122,
  SELECT_V2 = 123,