code: def flow_to_image(flow: torch.Tensor) -> torch.Tensor:
      """
      Converts a flow to an RGB image.
  
      Args:
          flow (Tensor): Flow of shape (N, 2, H, W) or (2, H, W) and dtype torch.float.
  
      Returns:
          img (Tensor): Image Tensor of dtype uint8 where each color corresponds
              to a given flow direction. Shape is (N, 3, H, W) or (3, H, W) depending on the input.
      """
  
      if flow.dtype != torch.float:
          raise ValueError(
              f"Flow should be of dtype torch.float, got {flow.dtype}."
          )
  
      orig_shape = flow.shape
      if flow.ndim == 3:
          flow = flow[None]  # Add batch dim
  
      if flow.ndim != 4 or flow.shape[1] != 2:
          raise ValueError(
              f"Input flow should have shape (2, H, W) or (N, 2, H, W), got {orig_shape}."
          )
  
      max_norm = torch.sum(flow**2, dim=1).sqrt().max()
      epsilon = torch.finfo((flow).dtype).eps
      normalized_flow = flow / (max_norm + epsilon)
      img = _normalized_flow_to_image(normalized_flow)
  
      if len(orig_shape) == 3:
          img = img[0]  # Remove batch dim
      return img
  
imports: import collections
  import math
  import pathlib
  import warnings
  from itertools import repeat
  from types import FunctionType
  from typing import Any, BinaryIO, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image, ImageColor, ImageDraw, ImageFont
label_desc: Use image node_id 6 for calling a global function with example usage: flow_to_image(flow) and returns: img