TF import with dyn shapes

dynamic.mlir

module  {
  func @binary_func(%arg0: tensor<16xf32>, %arg1: tensor<16xf32>) -> (tensor<16xf32>, tensor<16xf32>) attributes {iree.abi = "{\22a\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22r\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22v\22:1}", iree.module.export} {
    return %arg0, %arg1 : tensor<16xf32>, tensor<16xf32>
  }
  func @dict_nest(%arg0: !iree.list<?>, %arg1: tensor<f32>) -> !iree.list<?> attributes {iree.abi = "{\22a\22:[[\22sdict\22,[\22dict\22,[\22sdict\22,[\22a\22,[\22ndarray\22,\22f32\22,1,16]],[\22b\22,[\22ndarray\22,\22f32\22,1,16]]]],[\22list\22,[\22slist\22,[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]]]],[\22ndarray\22,\22f32\22,0]],\22r\22:[[\22sdict\22,[\22dict\22,[\22sdict\22,[\22a\22,[\22ndarray\22,\22f32\22,1,16]],[\22b\22,[\22ndarray\22,\22f32\22,1,16]]]],[\22list\22,[\22slist\22,[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]]]]],\22v\22:1}", iree.module.export} {
    %c2 = constant 2 : index
    %c0 = constant 0 : index
    %c1 = constant 1 : index
    %0 = iree.list.get %arg0[%c0] : !iree.list<?> -> !iree.list<?>
    %1 = iree.list.get %0[%c0] : !iree.list<?> -> tensor<16xf32>
    %2 = iree.list.get %0[%c1] : !iree.list<?> -> tensor<16xf32>
    %3 = iree.list.get %arg0[%c1] : !iree.list<?> -> !iree.list<?>
    %4 = iree.list.get %3[%c0] : !iree.list<?> -> tensor<16xf32>
    %5 = iree.list.get %3[%c1] : !iree.list<?> -> tensor<16xf32>
    %6 = iree.list.create %c2 : !iree.list<?>
    %7 = iree.list.create %c2 : !iree.list<?>
    iree.list.set %7[%c0], %1 : tensor<16xf32> -> !iree.list<?>
    iree.list.set %7[%c1], %2 : tensor<16xf32> -> !iree.list<?>
    iree.list.set %6[%c0], %7 : !iree.list<?> -> !iree.list<?>
    %8 = iree.list.create %c2 : !iree.list<?>
    iree.list.set %8[%c0], %4 : tensor<16xf32> -> !iree.list<?>
    iree.list.set %8[%c1], %5 : tensor<16xf32> -> !iree.list<?>
    iree.list.set %6[%c1], %8 : !iree.list<?> -> !iree.list<?>
    return %6 : !iree.list<?>
  }
  func @dynamic_addition(%arg0: tensor<?x16xf32>, %arg1: tensor<?x16xf32>) -> tensor<?x16xf32> attributes {iree.abi = "{\22a\22:[[\22ndarray\22,\22f32\22,2,null,16],[\22ndarray\22,\22f32\22,2,null,16]],\22r\22:[[\22ndarray\22,\22f32\22,2,null,1
6]],\22v\22:1}", iree.module.export} {
    %0 = shapex.get_ranked_shape %arg0 : tensor<?x16xf32> -> !shapex.ranked_shape<[?,16]>
    %1 = shapex.get_ranked_shape %arg1 : tensor<?x16xf32> -> !shapex.ranked_shape<[?,16]>
    %2 = "shapex.ranked_broadcast_shape"(%0, %1) {lhs_broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (!shapex.ranked_shape<[?,16]>, !shapex.ranked_shape<[?,16]>) -> !shapex.ranked_shape<[?,16]>
    %3 = "shapex.ranked_broadcast_in_dim"(%arg0, %2) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x16xf32>, !shapex.ranked_shape<[?,16]>) -> tensor<?x16xf32>
    %4 = "shapex.ranked_broadcast_in_dim"(%arg1, %2) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x16xf32>, !shapex.ranked_shape<[?,16]>) -> tensor<?x16xf32>
    %5 = mhlo.add %3, %4 : tensor<?x16xf32>
    return %5 : tensor<?x16xf32>
  }
  func @return_list(%arg0: tensor<16xf32>, %arg1: tensor<16xf32>) -> (tensor<16xf32>, tensor<16xf32>) attributes {iree.abi = "{\22a\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22r\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22v\22:1}", iree.module.export} {
    return %arg0, %arg1 : tensor<16xf32>, tensor<16xf32>
  }
  func @unary_func(%arg0: tensor<16xf32>) -> tensor<16xf32> attributes {iree.abi = "{\22a\22:[[\22ndarray\22,\22f32\22,1,16]],\22r\22:[[\22ndarray\22,\22f32\22,1,16]],\22v\22:1}", iree.module.export} {
    return %arg0 : tensor<16xf32>
  }
}