### 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",

false, "targets": [ { "expr": "max(rate(envoy_cluster_upstream_cx_rx_bytes_total{cluster_name=\"xds-grpc\"}[1m]))", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "XDS Response Bytes Max", "refId": "D" }, { "expr": "quantile(0.5, rate(envoy_cluster_upstream_cx_rx_bytes_total{cluster_name=\"xds-grpc\"}[1m]))", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "XDS Response Bytes Average", "refId": "B" }, { "expr": "max(rate(envoy_cluster_up...

// 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

      if (fc_op.getFilter() != filter) {
        // This filter goes through quantize and dequantize ops. Then we just
        // need to update the weight to the quantize op.
        filter.replaceAllUsesWith(new_filter_op);
      } else {
        // This filter doesn't go through quantize and dequantize ops, Then
        // we update the weight of the affine op directly.

    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));

  func.return %0 : tensor<8x8x8x8xf32>

  // CHECK-LABEL: fakeQuantArgsFalse
  // CHECK: "tfl.quantize"(%arg0) <{qtype = tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>}>
  // CHECK: %1 = "tfl.dequantize"(%0) : (tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>) -> tensor<8x8x8x8xf32>
}

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions | FileCheck --check-prefix=TF %s
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' | FileCheck --check-prefix=XLA %s

	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 (