[Prelude][Relay] Add utility function to extract tensor from prelude tensor_t

[wweic]

tensor_t in prelude is a type that wraps variable rank tensor with variable shape. Like, tensor_array_concat will return object of type tensor_t.

But for most of the other TF operators, they expect tensors with fixed rank. For example sum(tensor_array_concat(tensor_array)), it won't work right now since sum can't accept tensor_t returned by tensor_array_concat. We need functions to convert tensor_t to fixed rank tensors. They are:

let get_data_tensor1: tensor_t -> Tensor[(Any), dtype]
let get_data_tensor2: tensor_t -> Tensor[(Any, Any), dtype]
let get_data_tensor3: tensor_t -> Tensor[(Any, Any, Any), dtype]
let get_data_tensor4: tensor_t -> Tensor[(Any, Any, Any, Any), dtype]
let get_data_tensor5: tensor_t -> Tensor[(Any, Any, Any, Any, Any), dtype]
let get_data_tensor6: tensor_t -> Tensor[(Any, Any, Any, Any, Any, Any), dtype]

The reason we need 6 functions because relay can not represent a function has the following type:

let get_data_tensor: tensor_t -> Tensor[(Any), dtype] or Tensor[(Any, Any), dtype] or Tensor[(Any, Any, Any), dtype]

We also need a function get_tensor_rank: tensor_t -> Tensor[(), int32] so we can dispatch to the right get_data_tensor function given the rank of tensor_t.

Since we start to look into models with tensor array ops, we will have to implement these functions.