code: def data_dict():
      img = load_image(funcfile)
      # Here, I'm just doing this so I know that img.shape[0] is the number of
      # volumes
      t0_img = rollimg(img, "t")
      out = {"nimages": t0_img.shape[0]}
      # Below, I am just making a mask because I already have img, I know I can do
      # this. In principle, though, the pca function will just take another Image
      # as a mask
      img_data = t0_img.get_fdata()
      mask_cmap = drop_io_dim(img.coordmap, "t")
      first_frame = img_data[0]
      mask = Image(
          np.greater(first_frame, 500).astype(np.float64), mask_cmap
      )
      out["fmridata"] = img
      out["mask"] = mask
  
      # print data_dict['mask'].shape, np.sum(data_dict['mask'].get_fdata())
      assert out["mask"].shape == (17, 21, 3)
      assert_almost_equal(np.sum(out["mask"].get_fdata()), 1071.0)
      return out
  
imports: import numpy
  import pytest
  from nibabel.affines import from_matvec
  from numpy.testing import assert_almost_equal, assert_array_equal
  from ....core.api import AffineTransform, Image
  from ....core.api import CoordinateSystem
  from ....core.image.image import rollimg
  from ....core.reference.coordinate_map import AxisError, drop_io_dim
  from ....core.reference.coordinate_map import product
  from ....io.api import load_image
  from ....testing import funcfile
  from ..pca import pca
  from ..pca import pca_image
  from .test_pca import res2pos1
label_desc: Use image node_id 1 for calling a global function with example usage: data_dict() and returns: out