code: def create_one_image(shape, seed):
      rng = np.random.default_rng(seed)
      image = create_input(shape)
  
      # this test won't work for completely random warps - about a 10% error
      # so we create a single vector flow and rotate it randomly
      flow = np.stack(
          (
              np.ones(shape[0] * shape[-2] * shape[-1]),
              np.zeros(shape[0] * shape[-2] * shape[-1]),
          )
      ) * (rng.uniform() / 10)
  
      angle = rng.uniform() * 2 * np.pi
      cos_val, sin_val = np.cos(angle), np.sin(angle)
      rot_mat = np.array(((cos_val, -sin_val), (sin_val, cos_val)))
      flow = np.reshape(
          np.transpose(rot_mat @ flow),
          (shape[0], shape[-2], shape[-1], 2),
      )
      flow = torch.tensor(flow).to(image)
  
      tf_image = tf.convert_to_tensor(image.permute(0, 2, 3, 1).numpy())
      tf_flow = tf.convert_to_tensor(
          np.flip(flow.numpy(), -1) * ((np.array(shape[-2:]) - 1) / 2)
      )
      tf_image_warp = tfa.image.dense_image_warp(tf_image, -tf_flow)
  
      tf_image_warp = torch.tensor(tf_image_warp.numpy()).permute(
          0, 3, 1, 2
      )
  
      return {
          "image": image,
          "flow": flow,
          "tf_image_warp": tf_image_warp,
      }
  
imports: import pickle
  import numpy
  import tensorflow
  import tensorflow_addons
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: create_one_image(shape, seed) and returns: dict