docs: def image_predict(self, X):
          """
          Predicts class label for the entire image.
  
          Parameters:
          -----------
  
          X: array, shape = [n_samples, n_pixels_y, n_pixels_x, n_bands]
              Array of training images
  
          y: array, shape = [n_samples] or [n_samples, n_pixels_y, n_pixels_x]
              Target labels or masks.
          """
          self._check_image(X)
  
          patches, patches_shape = self._to_patches(X)
  
          predictions = self.classifier.predict(self._transform_input(patches))
  
          image_predictions = predictions.reshape(patches_shape[0:3])
  
          image_results = np.zeros((self._samples,) + self._image_size)
  
          nx, ny = self.receptive_field
          row_steps = self._image_size[0] // nx
          col_steps = self._image_size[1] // ny
  
          # how can this be optimised?
          for i, j, k in itertools.product(range(row_steps), range(col_steps), range(self._samples)):
              image_results[k, nx * i:nx * (i + 1), ny * j:ny * (j + 1)] = image_predictions[k, i, j]
  
          return image_results