code: def test_filmgls():
      input_map = dict(
          args=dict(argstr="%s"),
          autocorr_estimate_only=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="-ac",
          ),
          autocorr_noestimate=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="-noest",
          ),
          brightness_threshold=dict(argstr="-epith %d"),
          design_file=dict(argstr="%s"),
          environ=dict(usedefault=True),
          fit_armodel=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="-ar",
          ),
          full_data=dict(argstr="-v"),
          in_file=dict(mandatory=True, argstr="%s"),
          mask_size=dict(argstr="-ms %d"),
          multitaper_product=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="-mt %d",
          ),
          output_pwdata=dict(argstr="-output_pwdata"),
          output_type=dict(),
          results_dir=dict(usedefault=True, argstr="-rn %s"),
          smooth_autocorr=dict(argstr="-sa"),
          threshold=dict(argstr="%f"),
          tukey_window=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="-tukey %d",
          ),
          use_pava=dict(argstr="-pava"),
      )
      input_map2 = dict(
          args=dict(argstr="%s"),
          autocorr_estimate_only=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="--ac",
          ),
          autocorr_noestimate=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="--noest",
          ),
          brightness_threshold=dict(argstr="--epith=%d"),
          design_file=dict(argstr="--pd=%s"),
          environ=dict(usedefault=True),
          fit_armodel=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="--ar",
          ),
          full_data=dict(argstr="-v"),
          in_file=dict(mandatory=True, argstr="--in=%s"),
          mask_size=dict(argstr="--ms=%d"),
          multitaper_product=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="--mt=%d",
          ),
          output_pwdata=dict(argstr="--outputPWdata"),
          output_type=dict(),
          results_dir=dict(argstr="--rn=%s", usedefault=True),
          smooth_autocorr=dict(argstr="--sa"),
          threshold=dict(usedefault=True, argstr="--thr=%f"),
          tukey_window=dict(
              xor=[
                  "autocorr_estimate_only",
                  "fit_armodel",
                  "tukey_window",
                  "multitaper_product",
                  "use_pava",
                  "autocorr_noestimate",
              ],
              argstr="--tukey=%d",
          ),
          use_pava=dict(argstr="--pava"),
      )
      instance = FILMGLS()
      if isinstance(instance.inputs, FILMGLSInputSpec):
          for key, metadata in list(input_map.items()):
              for metakey, value in list(metadata.items()):
                  assert (
                      getattr(instance.inputs.traits()[key], metakey)
                      == value
                  )
      else:
          for key, metadata in list(input_map2.items()):
              for metakey, value in list(metadata.items()):
                  assert (
                      getattr(instance.inputs.traits()[key], metakey)
                      == value
                  )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_filmgls() without return types