}

func mustGetLabels(t test.Failer, obj object.K8sObject, path string) map[string]string {
	t.Helper()
	got := mustGetPath(t, obj, path)
	conv, ok := got.(map[string]any)
	if !ok {
		t.Fatalf("could not convert %v", got)
	}
	ret := map[string]string{}
	for k, v := range conv {
		sv, ok := v.(string)
		if !ok {
			t.Fatalf("could not convert to string %v", v)
		}
		ret[k] = sv
	}
	return ret
}

// CHECK-NEXT:  %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[q_cst_0]], %[[q_cst_1]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}>
// CHECK-NEXT:  %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[cst]]) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>)

// or `std::nullopt` if the given op is not per-channel quantizable.
std::optional<int64_t> GetDotGeneralQuantizationDim(
    ::mlir::stablehlo::DotGeneralOp dot_general_op);

// Checks if a `StringRef` contains 'conv' or 'dot_general'.
bool ContainsConvOrDot(StringRef str);

}  // namespace mlir::quant

		ir.ORECOVERFP,
		ir.OSTR2BYTES,
		ir.OSTR2BYTESTMP,
		ir.OSTR2RUNES:

		if isRuneCount(n) {
			// len([]rune(s)) is rewritten to runtime.countrunes(s) later.
			conv := n.(*ir.UnaryExpr).X.(*ir.ConvExpr)
			conv.X = o.expr(conv.X, nil)
		} else {
			o.call(n)
		}

		if lhs == nil || lhs.Op() != ir.ONAME || base.Flag.Cfg.Instrumenting {
			return o.copyExpr(n)
		}
		return n

        input_shape=(1, 3, 4, 3), filter_shape=(2, 3, 3, 2)
    )

    signatures = {
        'serving_default': model.conv.get_concrete_function(),
    }
    save_opts = save_options.SaveOptions(
        function_aliases={'conv_func': model.conv}
    )

    saved_model_save.save(
        model, self._input_saved_model_path, signatures, save_opts
    )

  // FakeQuant).
  bool disable_per_channel = false;

  // Whether to disable per-channel weight quantization and enable legacy per
  // tensor quantization. The legacy quantization for Dense layers is
  // inconsistent with Conv 1x1 which always performs per channel quantization.
  bool disable_per_channel_for_dense_layers = false;

  // Whether to use fixed output ranges of the activation ops (tanh, sigmoid,
  // etc.) and not infer weight constants.

func shouldClose(major, minor int, header Header, removeCloseHeader bool) bool {
	if major < 1 {
		return true
	}

	conv := header["Connection"]
	hasClose := httpguts.HeaderValuesContainsToken(conv, "close")
	if major == 1 && minor == 0 {
		return hasClose || !httpguts.HeaderValuesContainsToken(conv, "keep-alive")
	}

	if hasClose && removeCloseHeader {
		header.Del("Connection")
	}

	return hasClose
}

		}

		// hv2 := rune(ha[hv1])
		nind := ir.NewIndexExpr(base.Pos, ha, hv1)
		nind.SetBounded(true)
		body = append(body, ir.NewAssignStmt(base.Pos, hv2, typecheck.Conv(nind, types.RuneType)))

		// if hv2 < utf8.RuneSelf
		nif := ir.NewIfStmt(base.Pos, nil, nil, nil)
		nif.Cond = ir.NewBinaryExpr(base.Pos, ir.OLT, hv2, ir.NewInt(base.Pos, utf8.RuneSelf))

		// hv1++

    const auto float_graph = model_->subgraphs()->Get(subgraph_idx);
    ASSERT_EQ(quantized_graph->tensors()->size(),
              float_graph->tensors()->size());
    // Make sure the graph only has one Conv operation.
    ASSERT_EQ(quantized_graph->operators()->size(), 1);
    const auto op = quantized_graph->operators()->Get(0);
    const uint32_t op_code_idx = op->opcode_index();

			conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":