return true
		}
		// We may have skipped past one or more OCONVNOPs, so
		// use conv to ensure r is assignable to l (#13263).
		dst := ir.Node(l)
		if loff != 0 || !types.Identical(typ, l.Type()) {
			dst = ir.NewNameOffsetExpr(base.Pos, l, loff, typ)
		}
		s.append(ir.NewAssignStmt(base.Pos, dst, typecheck.Conv(r, typ)))
		return true

	case ir.ONIL:
		return true

	case ir.OLITERAL:

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

        "MLIR dump file name.">,
    Option<"merge_fusion_with_dequantize_",
        "merge-fusion-with-dequantize",
        "bool", /*default=*/"false",
        "Whether to merge quantized conv/dot_general fusion with subsequent dequantize.">,
  ];
  let dependentDialects = [
    "mlir::arith::ArithDialect",
    "mlir::stablehlo::StablehloDialect",
    "mlir::quant::QuantizationDialect",

		if types.SimType[src.Kind()] == types.SimType[dst.Kind()] {
			return ir.OCONVNOP, ""
		}
		return ir.OCONV, ""
	}

	// 5. src and dst are both complex types.
	if src.IsComplex() && dst.IsComplex() {
		if types.SimType[src.Kind()] == types.SimType[dst.Kind()] {
			return ir.OCONVNOP, ""
		}
		return ir.OCONV, ""
	}

	// Special case for constant conversions: any numeric

  // The result of the conv must be the first input to BiasAdd to be fusable.
  %1 = "tf.BiasAdd"(%arg0, %0) {data_format = "NHWC"} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  %2 = "tf.Elu"(%1) : (tensor<*xf32>) -> tensor<*xf32>

				a.Type = obj.TYPE_MEM
			}
			// fmt.Printf("CONST %d %s\n", a.Offset, obj.Dconv(&emptyProg, 0, a))
			p.expectOperandEnd()
			return
		}
		// fmt.Printf("offset %d \n", a.Offset)
	}

	// Register indirection: (reg) or (index*scale). We are on the opening paren.
	p.registerIndirect(a, prefix)
	// fmt.Printf("DONE %s\n", p.arch.Dconv(&emptyProg, 0, a))

	p.expectOperandEnd()
	return
}

// these tests pass with `-gcflags=-l`.
func float32bits(f float32) uint32 { // ERROR "can inline float32bits"
	return *(*uint32)(unsafe.Pointer(&f))
}

// Ensure OCONVNOP is zero cost.
func Conv(v uint64) uint64 { // ERROR "can inline Conv"
	return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)"
}
func conv2(v uint64) uint64 { // ERROR "can inline conv2"

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

// these tests pass with `-gcflags=-l`.
func float32bits(f float32) uint32 { // ERROR "can inline float32bits"
	return *(*uint32)(unsafe.Pointer(&f))
}

// Ensure OCONVNOP is zero cost.
func Conv(v uint64) uint64 { // ERROR "can inline Conv"
	return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)"
}
func conv2(v uint64) uint64 { // ERROR "can inline conv2"