code: def resolve_interpolation_mode(interpolation_type: str):
      """
      Maps a string describing an interpolation function to the corresponding torchvision `InterpolationMode` enum. The
      full list of supported enums is documented at
      https://pytorch.org/vision/0.9/transforms.html#torchvision.transforms.functional.InterpolationMode.
  
      Args:
          interpolation_type (`str`):
              A string describing an interpolation method. Currently, `bilinear`, `bicubic`, `box`, `nearest`,
              `nearest_exact`, `hamming`, and `lanczos` are supported, corresponding to the supported interpolation modes
              in torchvision.
  
      Returns:
          `torchvision.transforms.InterpolationMode`: an `InterpolationMode` enum used by torchvision's `resize`
          transform.
      """
      if interpolation_type == "bilinear":
          interpolation_mode = transforms.InterpolationMode.BILINEAR
      elif interpolation_type == "bicubic":
          interpolation_mode = transforms.InterpolationMode.BICUBIC
      elif interpolation_type == "box":
          interpolation_mode = transforms.InterpolationMode.BOX
      elif interpolation_type == "nearest":
          interpolation_mode = transforms.InterpolationMode.NEAREST
      elif interpolation_type == "nearest_exact":
          interpolation_mode = (
              transforms.InterpolationMode.NEAREST_EXACT
          )
      elif interpolation_type == "hamming":
          interpolation_mode = transforms.InterpolationMode.HAMMING
      elif interpolation_type == "lanczos":
          interpolation_mode = transforms.InterpolationMode.LANCZOS
      else:
          raise ValueError(
              f"The given interpolation mode {interpolation_type} is not supported. Currently supported interpolation"
              f" modes are `bilinear`, `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`."
          )
  
      return interpolation_mode
  
imports: import contextlib
  import copy
  import random
  from typing import Any, Dict, Iterable, List, Optional, Union
  import numpy
  import torch
  from torchvision import transforms
  from .models import UNet2DConditionModel
  from .utils import convert_state_dict_to_diffusers, convert_state_dict_to_peft, deprecate, is_peft_available, is_transformers_available
label_desc: Use image node_id 3 for calling a global function with example usage: resolve_interpolation_mode(interpolation_type) and returns: interpolation_mode