code: def broadcast_tensors(*input: DTensor):
      for t in input[1:]:
          if isinstance(t, DTensor):
              if t.shardings != input[0].shardings:
                  raise RuntimeError(
                      "broadcast_tensors expects all inputs to be broadcastable to the first input"
                  )
          if (
              torch.broadcast_shapes(input[0].shape, t.shape)
              != input[0].shape
          ):
              raise RuntimeError(
                  "broadcast_tensors expects all inputs to be broadcastable to the first input"
              )
      return input
  
label_desc: Use image node_id 1 for calling a global function with example usage: broadcast_tensors() and returns: input