%0 = "tf.StridedSlice"(%arg0, %cst, %cst, %cst_0) {begin_mask = 6 : i64, ellipsis_mask = 0 : i64, end_mask = 6 : i64, new_axis_mask = 9 : i64, shrink_axis_mask = 0 : i64} : (tensor<?x?xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>
  func.return %0 : tensor<1x?x?x1xf32>

  // CHECK-NOT: "tf.Reshape"
  // CHECK: "tf.StridedSlice"
}

// CHECK-LABEL: @StridedSliceRewriteMasks

  %outputs_12  =  "tf.Shape"(%arg1) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<?x4xf32>) -> tensor<2xi32>
  // CHECK: [[max_iter:%.*]] = "tf.StridedSlice"

    auto shape = rewriter.create<TF::ConstOp>(loc, shape_type, shape_attr);

    return rewriter.create<TF::ReshapeOp>(loc, result_type, filter, shape);
  }
};

// StridedSlice can have complicated attributes like begin_axis_mask,
// end_axis_mask, ellipsis_axis_mask, new_axis_mask, shrink_axis_mask. These
// masks will complicate the strided_slice computation logic, we can simplify

      "ConcatOffset", "Const", "MirrorPad", "MirrorPadGrad", "Pack", "Pad",
      "PadV2", "Reverse", "ReverseV2", "ReverseSequence", "Slice", "Split",
      "SplitV", "StridedSlice", "StridedSliceGrad",
      "ResourceStridedSliceAssign", "Tile", "Transpose", "InvertPermutation",
      "Unpack", "DeviceIndex", "TensorStridedSliceUpdate", "XlaConcatND",
      "XlaSplitND",
     }}};