code: def _as_floats(image0, image1):
      """
      Promote im1, im2 to nearest appropriate floating point precision.
      """
      float_type = _supported_float_type((image0.dtype, image1.dtype))
      image0 = np.asarray(image0, dtype=float_type)
      image1 = np.asarray(image1, dtype=float_type)
      return image0, image1
  
file_path: scikit-image/skimage/metrics/simple_metrics.py
imports: import numpy
  from scipy.stats import entropy
  from ..util.dtype import dtype_range
  from .._shared.utils import _supported_float_type, check_shape_equality, warn
label_desc: Use image node_id 1 for calling a global function with example usage: _as_floats(image0, image1) and returns: image0,  image1