code: def compute_feats(image, kernels):
      feats = np.zeros((len(kernels), 2), dtype=np.double)
      for k, kernel in enumerate(kernels):
          filtered = ndi.convolve(image, kernel, mode="wrap")
          feats[k, 0] = filtered.mean()
          feats[k, 1] = filtered.var()
      return feats
  
file_path: scikit-image/doc/examples/features_detection/plot_gabor.py
imports: import matplotlib.pyplot
  import numpy
  from scipy import ndimage
  from skimage import data
  from skimage.util import img_as_float
  from skimage.filters import gabor_kernel
label_desc: Use image node_id 1 for calling a global function with example usage: compute_feats(image, kernels) and returns: feats