class_docstr: Allows to perform MTCNN Detection ->
      a) Detection of faces (with the confidence probability)
      b) Detection of keypoints (left eye, right eye, nose, mouth_left, mouth_right)
class_docstr_tok: ["Allows","to","perform","MTCNN","Detection","-",">","a",")","Detection","of","faces","(","with","the","confidence","probability",")","b",")","Detection","of","keypoints","(","left","eye",",","right","eye",",","nose",",","mouth_left",",","mouth_right",")"]
docstr: Scales the image to a given scale.
  :param image:
  :param scale:
  :return:
docstr_tok: ["Scales","the","image","to","a","given","scale",".",":","param","image",":",":","param","scale",":",":","return",":"]
code: def __scale_image(image, scale: float):
      """
      Scales the image to a given scale.
      :param image:
      :param scale:
      :return:
      """
      height, width, _ = image.shape
  
      width_scaled = int(np.ceil(width * scale))
      height_scaled = int(np.ceil(height * scale))
  
      im_data = cv2.resize(
          image,
          (width_scaled, height_scaled),
          interpolation=cv2.INTER_AREA,
      )
  
      # Normalize the image's pixels
      im_data_normalized = (im_data - 127.5) * 0.0078125
  
      return im_data_normalized
  
imports: import cv2
  import numpy
  import pkg_resources
  from mtcnn.exceptions import InvalidImage
  from mtcnn.network.factory import NetworkFactory
label_desc: Use image node_id 5 for calling the MTCNN obj's underlying member method code with example usage: obj.__scale_image(image, scale) and returns: im_data_normalized