If we can get `Any`(https://github.com/dmlc/tvm/issues/3042) merged, I think we can support TensorArray as follows: ```` type dynamic_tensor = Tensor0 of TensorType(shape=()) | Tensor1 of TensorType(shape=(Any)) | Tensor2 of TensorType(shape=(Any, Any)) | Tensor3 of TensorType(shape=(Any, Any, Any)) | Tensor4 of TensorType(shape=(Any, Any, Any, Any)) | Tensor5 of TensorType(shape=(Any, Any, Any, Any, Any)) | Tensor6 of TensorType(shape=(Any, Any, Any, Any, Any, Any))
type tensor_array = dynamic_tensor list ```` We define an data type `dynamic_tensor` that supports tensors up to 6(we can grow the rank of cause but might not be necessary). Then tensor array is just a `dynamic_tensor` list. Then we can implement TensorArray ops as relay functions. Most of them are trivial to implement. Some are tricky( but I think doable with `expand_dims `): * TensorArrayConcat * TensorArrayStack * TensorArrayUnstack --- [Visit Topic](https://discuss.tvm.ai/t/how-to-support-tf-tensorarray/1983/3) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/8beced698fa80aa02c6a32ce0152013fd43ad6ade309efab5e1614c9f02df4c9). Tianqi Chen, UW, Seattle, WA, 98105, United States http://tracking.discuss.tvm.ai/tracking/unsubscribe?msgid=mKqzfOWjn5EzGWA5fP_2dA2
