title: Unrecognized options: {'target'} when using @jit(target="cuda")
body: <p>I have optimized some python code using the decorator @jit from numba library. However, I want to indicate to @jit to use my GPU device explicitly. From: <a href="https://stackoverflow.com/questions/35890045/difference-between-cuda-jit-and-jittarget-gpu">Difference between @cuda.jit and @jit(target=&#39;gpu&#39;)</a>, I understand that I need to use @jit(target=&quot;cuda&quot;) to do it.</p>
  <p>I tried to do it by doing something like this:</p>
  <pre><code>from numba import jit, cuda
  
  @jit(target='cuda')  # The code runs normally without (target='cuda')
  def function(args):
      # some code
  </code></pre>
  <p>And I got the following error:</p>
  <pre><code>KeyError: &quot;Unrecognized options: {'target'}. Known options are dict_keys(['_nrt', 'boundscheck', 'debug', 'error_model', 'fastmath', 'forceinline', 'forceobj', 'inline', 'looplift', 'no_cfunc_wrapper', 'no_cpython_wrapper', 'no_rewrites', 'nogil', 'nopython', 'parallel', 'target_backend'])&quot;
  </code></pre>
  <p>I have read this: <a href="https://stackoverflow.com/questions/62794341/how-to-run-numba-jit-decorated-function-on-gpu">How to run numba.jit decorated function on GPU?</a> but the solution did not work.</p>
  <p>I would appreciate some help to make @jit(target='cuda') work without rewriting the code using @cuda.jit as this last one is for writing CUDA kernel in Python and compile and run it.</p>
  <p>Many thanks in advance!</p>