}

// 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"
	return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1"
}
func conv1(v uint64) uint64 { // ERROR "can inline conv1"

}

// 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"
	return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1"
}
func conv1(v uint64) uint64 { // ERROR "can inline conv1"

  // CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32>
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>

// CHECK: %[[conv3d:.*]] = "tfl.conv_3d"(%arg0, %[[w]], %[[const]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<?x28x28x28x8xf32>, tensor<3x3x3x8x16xf32>, none) -> tensor<?x26x26x26x16xf32>
// CHECK: %2 = "tfl.shape"(%[[conv3d]]) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>

   (HasRank $input),
   (HasRank $weight),
   (IsInt32ElementType $matmul)],
  [], (addBenefit 10)>;


// Converts inlined Conv3D pattern to TF XlaConvV2 op. This pattern
// doesn't support non-constant weights.
def ConvertTFConv3DToXLAConvOp : Pat<
  (TF_CastOp:$conv
    (TF_Conv3DOp
      (TF_CastOp:$cast_input
        (TF_SubOp (TF_CastOp $input, $truncate1), $input_zp), $truncate2),
      (TF_CastOp

  %b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {

	init.Append(as)
	return res
}

// Returns the data word (the second word) used to represent conv.X in
// an interface.
func dataWord(conv *ir.ConvExpr, init *ir.Nodes) ir.Node {
	pos, n := conv.Pos(), conv.X
	fromType := n.Type()

	// If it's a pointer, it is its own representation.
	if types.IsDirectIface(fromType) {
		return n
	}

    %zp_offset: tensor<?x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<?x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]
  // CHECK-DAG: %[[result:.*]] = chlo.broadcast_add %[[conv]], %[[combined]]
  // CHECK: return %[[result]]
  %0 = mhlo.convolution(%lhs, %rhs)
      dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],

		return mkcall("countrunes", n.Type(), init, typecheck.Conv(n.X.(*ir.ConvExpr).X, types.Types[types.TSTRING]))
	}
	if isByteCount(n) {
		conv := n.X.(*ir.ConvExpr)
		walkStmtList(conv.Init())
		init.Append(ir.TakeInit(conv)...)
		_, len := backingArrayPtrLen(cheapExpr(conv.X, init))
		return len
	}
	if isChanLenCap(n) {
		name := "chanlen"
		if n.Op() == ir.OCAP {

  %b = arith.constant dense<-1.23697901> : tensor<64xf32>
  %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32>
  func.return %conv : tensor<1x112x112x64xf32>