if (!result_type) {
      return rewriter.notifyMatchFailure(mul_op, [&](::mlir::Diagnostic &diag) {
        diag << "entities 'filter, multiplier' failed to satisfy constraint: "
                "non-broadcastable operands";
      });
    }
    filter_value = filter.getValue();
    mul_value = multiplier.getValue();
    // In MHLO, Conv filter is in HWIO format, Depthwise conv filter is in HW1O

  // CHECK: return %[[CONV]] : tensor<1x28x23x2xf32>
}

// CHECK-LABEL: @notfuseMulIntoConv2d
// filter and multiply are not broadcastable
func.func @notfuseMulIntoConv2d(%arg0: tensor<1x112x112x3xf32>) -> tensor<1x28x23x2xf32> {

  auto result_type =
      OpTrait::util::getBroadcastedType(x.getType(), y.getType());
  if (!result_type) {
    if (incompatible_shape_error.getValue()) {
      mlir::emitError(loc, "non-broadcastable operands");
    } else {
      return UnrankedTensorType::get(builder->getI1Type());
    }
  }

  auto ranked_type = mlir::dyn_cast<RankedTensorType>(result_type);

  "$1.getType().cast<ShapedType>().hasRank() && "
  "$0.getType().cast<ShapedType>().getShape() == $1.getType().cast<ShapedType>().getShape()">,
  "Checks if the shapes of tensors are same.">;

// Make the 1D value $0 broadcastable with the shape of $1.
def MakeOneDimValueBroadcastable : NativeCodeCall<
  "MakeOneDimValueBroadcastable($_builder, $_loc, $0, $1.getType().cast<ShapedType>())">;

  func.return %1: tensor<2xi32>
}

// CHECK-LABEL: func @broadcast_add
// TODO(laurenzo): Change this to a (5 + 2x1) shaped add to make the check
// patterns unambiguous and more interesting (once broadcastable trait is
// fixed upstream).
func.func @broadcast_add(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi32> {

  binding_output = op->getResult(0);
  binding_input = op->getOperand(0);
  binding_weight = op->getOperand(1);
  return success();
}

// Makes the 1D value broadcastable with the `rhs_shape`.
Value MakeOneDimValueBroadcastable(OpBuilder& builder, Location loc,
                                   Value value, ShapedType rhs_shape) {

  }
  return new_shape;
}

// Determine if op commutes with transposes. Requires a strict
// definition of Elementwise, all i/o shapes and types must be same-rank
// broadcastable and fully static. Consider moving this into attribute later.
bool IsElementwise(Operation *op) {
  if (!(llvm::isa<TFL::AddOp, TFL::MulOp, TFL::DivOp, TFL::SubOp,
                  TFL::MaximumOp, TFL::MinimumOp>(op))) {

        tf_type::DropRefAndSubTypes(output_tensor_type));
    if (!broadcasted_type) {
      return switchn.emitOpError()
             << "expects data operand to be broadcastable with all output types"
             << " but got " << operand0_tensor_type << " vs "
             << output_tensor_type;
    }
  }
  return success();
}

void SwitchNOp::print(OpAsmPrinter &p) {

bool IsBroadcastableElementsAttrAndType(Type a, Type b) {
  return OpTrait::util::getBroadcastedType(a, b) != Type();
}

// Returns whether the resultant type of any broadcastable operation with
// operands `a` and `b` matches `expected_output`. Returns false if `a` is not
// broadcast-compatible with `b`.
bool OperandsBroadcastToOutputType(Type a, Type b, Type expected_output) {

//===----------------------------------------------------------------------===//

// Check that two values can be broadcasted together
def AreBroadcastCompatible : Constraint<CPred<"AreBroadcastCompatible($0, $1)">,
    "types must be broadcastable">;

class DirectBinaryPat<Op FromOp, Op ToOp>
  : Pat<(FromOp AnyTensor:$l, AnyTensor:$r),
        (ToOp $l, $r, (BinBroadcastDimensions $l, $r))>;

foreach fromToBinPair = [[TF_AddV2Op, CHLO_BroadcastAddOp],