code: def make_stereo_flow(
      flow: Union[torch.Tensor, List[torch.Tensor]],
      model_out_channels: int,
  ) -> torch.Tensor:
      """Helper function to make stereo flow from a given model output"""
      if isinstance(flow, list):
          return [
              make_stereo_flow(flow_i, model_out_channels)
              for flow_i in flow
          ]
  
      B, C, H, W = flow.shape
      # we need to add zero flow if the model outputs 2 channels
      if C == 1 and model_out_channels == 2:
          zero_flow = torch.zeros_like(flow)
          # by convention the flow is X-Y axis, so we need the Y flow last
          flow = torch.cat([flow, zero_flow], dim=1)
      return flow
  
imports: import argparse
  import os
  import warnings
  from pathlib import Path
  from typing import List, Union
  import numpy
  import torch
  import torch.distributed
  import torchvision.models.optical_flow
  import torchvision.prototype.models.depth.stereo
  import utils
  import visualization
  from parsing import make_dataset, make_eval_transform, make_train_transform, VALID_DATASETS
  from torch import nn
  from torchvision.transforms.functional import get_dimensions, InterpolationMode, resize
  from utils.metrics import AVAILABLE_METRICS
  from utils.norm import freeze_batch_norm
label_desc: Use image node_id 1 for calling a global function with example usage: make_stereo_flow(flow, model_out_channels) and returns: flow, unknown