func.return %rst : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<16x3x3x3xf32>
// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<16x3x3x3x!quant.uniform<u4:f32, 1.000000e+00>>}>

  func.return %rst : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<16x3x3x3xf32>
// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<16x3x3x3x!quant.uniform<u8:f32, 1.000000e+00>>}>

// function signature. Constants are also transposed accordingly.

// CHECK-LABEL: nchw_conv_with_bias_add
// CHECK-SAME: %[[ARG:.+]]: tensor<1x2x5x5xf32>
func.func @nchw_conv_with_bias_add(%arg0: tensor<1x2x5x5xf32>) -> tensor<1x4x5x5xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<4x2x3x3xf32>
  %1 = stablehlo.constant dense<3.000000e+00> : tensor<1x4x5x5xf32>

// only be used as the shape-determining operand; it will not replace other
// usages of the original constant. If the operands are not constants (i.e.
// results of some other computation), then the pass recursively traverses the
// call tree upwards and duplicates all constants found in the subtree in a
// similar manner.
//
// This pass may be used to avoid placing shape-determining constants in the CPU

package constdecl

import "math"
import "unsafe"

var v int

// Const decls must be initialized by constants.
const _ = v /* ERROR "not constant" */
const _ = math /* ERROR "not constant" */ .Sin(0)
const _ = int /* ERROR "not an expression" */

func _() {
	const _ = v /* ERROR "not constant" */
	const _ = math /* ERROR "not constant" */ .Sin(0)
	const _ = int /* ERROR "not an expression" */
}

// for debugging purposes.
func (po *poset) CheckIntegrity() {
	// Record which index is a constant
	constants := newBitset(int(po.lastidx + 1))
	for _, c := range po.constants {
		constants.Set(c)
	}

	// Verify that each node appears in a single DAG, and that
	// all constants are within the first DAG
	seen := newBitset(int(po.lastidx + 1))
	for ridx, r := range po.roots {
		if r == 0 {

    }

    /**
     * Consume "accessible" dependents of a constant. Accessible dependents in this context
     * are dependents that have a constant calculated from constant from origin.
     *
     * Example of accessible dependent:
     * class A {
     *     public static final int CALCULATE_ACCESSIBLE_CONSTANT = CONSTANT;
     * }
     */

  inputs.reserve(operands.size());
  for (auto input : operands) {
    inputs.push_back(mlir::cast<ElementsAttr>(input));
  }

  SmallVector<Attribute> constants;
  LogicalResult status = EvaluateOperation(inst, inputs, constants);
  results.assign(constants.begin(), constants.end());
  return status;
}

static bool init_hooks = ([] () {
  TensorFlowDialect::RegisterConstantFoldHook(ConstantFoldFallbackHook);
}(), true);

    @NotYetImplemented
    // Currently not implemented, since it's expensive to track constants and their values
    def "ignores irrelevant changes to constant values"() {
        source api: ["class A {}", "class B { final static int x = 3; final static int y = -2; }"],

        val bindingContext = analysisContext.analyze(expression)

        val constant = ConstantExpressionEvaluator.getPossiblyErrorConstant(expression, bindingContext)

        // TODO: how to _not_ evaluate expressions with a compilation error, e.g., uninitialized property access
        if (constant?.usesNonConstValAsConstant == true) return null

        return constant?.toConstantValue(TypeUtils.NO_EXPECTED_TYPE)?.toKtConstantValue()
    }