// CHECK: %[[UPDATE1:.*]] = "tf.XlaDynamicUpdateSlice"(%[[READ1]],
// CHECK: "tf.AssignVariableOp"(%[[CARG1]], %[[UPDATE1]])
// CHECK: %[[READ2:.*]] = "tf.ReadVariableOp"(%[[CARG2]]) : (tensor<!tf_type.resource<tensor<5x3xf32>>>) -> tensor<5x3xf32>
// CHECK: %[[UPDATE2:.*]] = "tf.XlaDynamicUpdateSlice"(%[[READ2]],
// CHECK: "tf.AssignVariableOp"(%[[CARG2]], %[[UPDATE2]])

for `tf.EmptyTensorList` or the specified size for `tf.TensorListReserve`.
Each push will be turned into `tf.XlaDynamicUpdateSlice` with the incremented
size, and each pop will be turned into a `tf.Slice` and a copy of the buffer
with decremented size. Each `tf.TensorListSetItem` will be turned into a
`tf.XlaDynamicUpdateSlice` with unchanged size, and each `tf.TensorListGetItem`
will be rewritten to a `tf.Slice`.

  // |index| is expanded to [index, 0, 0, ...] as the indices to input_handle.
  // On a high level, it's doing something like:
  // def : Pat<(TensorListSetItem($input_handle, $index, $item)),
  //           (XlaDynamicUpdateSlice($input_handle, ExpandDims($item, 0),
  //              Concat(ExpandDims($index, 0), [0, 0, 0, ...])))>
  LogicalResult matchAndRewriteImplWithDynamicUpdateSlice(
      TF::TensorListSetItemOp op, OpAdaptor adaptor,

      "XlaConv",
      "XlaConvV2",
      "XlaCustomCall",
      "XlaCustomCallV2",
      "XlaDequantize",
      "XlaDot",
      "XlaDotV2",
      "XlaDynamicSlice",
      "XlaDynamicUpdateSlice",
      "XlaEinsum",
      "XlaGather",
      "XlaIf",
      "XlaKeyValueSort",
      "XlaOptimizationBarrier",
      "XlaPad",
      "XlaRecv",
      "XlaReduce",