code: def __init__(
      self,
      n_stages=1,
      method="bilinear",
      multiplier=0.5,
      in_channels=3,
      out_channels=None,
      bias=False,
  ):
      super().__init__()
      self.n_stages = n_stages
      assert self.n_stages >= 0
      assert method in [
          "nearest",
          "linear",
          "bilinear",
          "trilinear",
          "bicubic",
          "area",
      ]
      self.multiplier = multiplier
      self.interpolator = partial(
          torch.nn.functional.interpolate, mode=method
      )
      self.remap_output = out_channels is not None
      if self.remap_output:
          print(
              f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
          )
          self.channel_mapper = nn.Conv2d(
              in_channels, out_channels, 1, bias=bias
          )
  
imports: import torch
  import torch.nn
  from functools import partial
  import clip
  from einops import rearrange, repeat
  from transformers import CLIPTokenizer, CLIPTextModel
  import kornia
  from ldm.modules.x_transformer import Encoder, TransformerWrapper
label_desc: Use image node_id 1 to create a new SpatialRescaler object from inherited base classes: nn with example: obj = SpatialRescaler(n_stages, method, multiplier, in_channels, out_channels, bias)