code: def predict(
      self,
      input_image_array,
      by_patch_of_size=None,
      batch_size=10,
      padding_size=2,
  ):
      """
      Processes the image array into a suitable format
      and transforms the network output in a suitable image format.
  
      Args:
          input_image_array: input image array.
          by_patch_of_size: for large image inference. Splits the image into
              patches of the given size.
          padding_size: for large image inference. Padding between the patches.
              Increase the value if there is seamlines.
          batch_size: for large image inferce. Number of patches processed at a time.
              Keep low and increase by_patch_of_size instead.
      Returns:
          sr_img: image output.
      """
  
      if by_patch_of_size:
          lr_img = process_array(input_image_array, expand=False)
          patches, p_shape = split_image_into_overlapping_patches(
              lr_img,
              patch_size=by_patch_of_size,
              padding_size=padding_size,
          )
          # return patches
          for i in range(0, len(patches), batch_size):
              batch = self.model.predict(patches[i : i + batch_size])
              if i == 0:
                  collect = batch
              else:
                  collect = np.append(collect, batch, axis=0)
  
          scale = self.scale
          padded_size_scaled = tuple(
              np.multiply(p_shape[0:2], scale)
          ) + (3,)
          scaled_image_shape = tuple(
              np.multiply(input_image_array.shape[0:2], scale)
          ) + (3,)
          sr_img = stich_together(
              collect,
              padded_image_shape=padded_size_scaled,
              target_shape=scaled_image_shape,
              padding_size=padding_size * scale,
          )
  
      else:
          lr_img = process_array(input_image_array)
          sr_img = self.model.predict(lr_img)[0]
  
      sr_img = process_output(sr_img)
      return sr_img
  
label_desc: Use image node_id 1 for calling the ImageModel obj's underlying member method code with example usage: obj.predict(input_image_array, by_patch_of_size, batch_size, padding_size) and returns: sr_img