Thank you for your reply.

I used yolov3 model from yolov3, and I find the 5d tile op from

tensorflow

``%578 = “tf.Reshape”(%577, %cst_0) {device = “”} : (tensor<16x52x52x255xf32>, tensor<5xi32>) → tensor<16x52x52x3x85xf32>

%579 = “tf.StridedSlice”(%578, %cst_8, %cst_11, %cst_7) {begin_mask = 15 : i64, device = “”, ellipsis_mask = 0 : i64, end_mask = 15 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<16x52x52x3x85xf32>, tensor<5xi

32>, tensor<5xi32>, tensor<5xi32>) → tensor<16x52x52x3x2xf32>

%580 = “tf.Sigmoid”(%579) {device = “”} : (tensor<16x52x52x3x2xf32>) → tensor<16x52x52x3x2xf32>

%581 = “tf.StridedSlice”(%578, %cst_11, %cst_10, %cst_7) {begin_mask = 15 : i64, device = “”, ellipsis_mask = 0 : i64, end_mask = 15 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<16x52x52x3x85xf32>, tensor<5x

i32>, tensor<5xi32>, tensor<5xi32>) → tensor<16x52x52x3x2xf32>

%582 = “tf.Exp”(%581) {device = “”} : (tensor<16x52x52x3x2xf32>) → tensor<16x52x52x3x2xf32>

%583 = “tf.Mul”(%582, %cst_13) {device = “”} : (tensor<16x52x52x3x2xf32>, tensor<3x2xf32>) → tensor<16x52x52x3x2xf32>

%584 = “tf.Mul”(%583, %cst_12) {device = “”} : (tensor<16x52x52x3x2xf32>, tensor) → tensor<16x52x52x3x2xf32>

%585 = “tf.StridedSlice”(%578, %cst_10, %cst_9, %cst_7) {begin_mask = 15 : i64, device = “”, ellipsis_mask = 0 : i64, end_mask = 15 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<16x52x52x3x85xf32>, tensor<5xi

32>, tensor<5xi32>, tensor<5xi32>) → tensor<16x52x52x3x1xf32>

%586 = “tf.Sigmoid”(%585) {device = “”} : (tensor<16x52x52x3x1xf32>) → tensor<16x52x52x3x1xf32>

%587 = “tf.StridedSlice”(%578, %cst_9, %cst_8, %cst_7) {begin_mask = 15 : i64, device = “”, ellipsis_mask = 0 : i64, end_mask = 31 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<16x52x52x3x85xf32>, tensor<5xi3

2>, tensor<5xi32>, tensor<5xi32>) → tensor<16x52x52x3x80xf32>

%588 = “tf.Sigmoid”(%587) {device = “”} : (tensor<16x52x52x3x80xf32>) → tensor<16x52x52x3x80xf32>

%589 = “tf.Tile”(%cst, %cst_1) {device = “”} : (tensor<1x52x52x1x2xi32>, tensor<5xi32>) → tensor<16x52x52x3x2xi32>

%590 = “tf.Cast”(%589) {Truncate = false, device = “”} : (tensor<16x52x52x3x2xi32>) → tensor<16x52x52x3x2xf32>

%591 = “tf.AddV2”(%580, %590) : (tensor<16x52x52x3x2xf32>, tensor<16x52x52x3x2xf32>) → tensor<16x52x52x3x2xf32>

%592 = “tf.Mul”(%591, %cst_12) {device = “”} : (tensor<16x52x52x3x2xf32>, tensor) → tensor<16x52x52x3x2xf32>

%593 = “tf.ConcatV2”(%592, %584, %cst_6) {device = “”} : (tensor<16x52x52x3x2xf32>, tensor<16x52x52x3x2xf32>, tensor) → tensor<16x52x52x3x4xf32>

%594 = “tf.ConcatV2”(%593, %586, %588, %cst_6) {device = “”} : (tensor<16x52x52x3x4xf32>, tensor<16x52x52x3x1xf32>, tensor<16x52x52x3x80xf32>, tensor) → tensor<16x52x52x3x85xf32>

return %594, %561, %528 : tensor<16x52x52x3x85xf32>, tensor<16x26x26x3x85xf32>, tensor<16x13x13x3x85xf32>

``

Is this model different from yours?