code: def test_pad_if_needed_position(params, image_shape):
      random.seed(42)
  
      image = np.zeros(image_shape)
      pad = A.PadIfNeeded(**params)
      image_padded = pad(image=image)["image"]
  
      true_result = np.ones(
          (
              max(image_shape[0], params["min_height"]),
              max(image_shape[1], params["min_width"]),
          )
      )
  
      if params["position"] == "center":
          x_start = image_shape[0] // 2
          y_start = image_shape[1] // 2
          true_result[
              x_start : x_start + image_shape[0],
              y_start : y_start + image_shape[1],
          ] = 0
          assert (image_padded == true_result).all()
  
      elif params["position"] == "top_left":
          true_result[: image_shape[0], : image_shape[1]] = 0
          assert (image_padded == true_result).all()
  
      elif params["position"] == "top_right":
          true_result[: image_shape[0], -image_shape[1] :] = 0
          assert (image_padded == true_result).all()
  
      elif params["position"] == "bottom_left":
          true_result[-image_shape[0] :, : image_shape[1]] = 0
          assert (image_padded == true_result).all()
  
      elif params["position"] == "bottom_right":
          true_result[-image_shape[0] :, -image_shape[1] :] = 0
          assert (image_padded == true_result).all()
  
      elif params["position"] == "random":
          true_result[0:5, -7:-1] = 0
          assert (image_padded == true_result).all()
  
imports: import random
  from typing import Dict, Tuple, Type
  import cv2
  import numpy
  import pytest
  import albumentations
  from .utils import get_dual_transforms, get_image_only_transforms, get_transforms
label_desc: Use image node_id 16 for calling a global function with example usage: test_pad_if_needed_position(params, image_shape) without return types