code: def get_input(self, batch, k):
      x = batch[k]
      if len(x.shape) == 3:
          x = x[..., None]
      x = (
          x.permute(0, 3, 1, 2)
          .to(memory_format=torch.contiguous_format)
          .float()
      )
      if self.batch_resize_range is not None:
          lower_size = self.batch_resize_range[0]
          upper_size = self.batch_resize_range[1]
          if self.global_step <= 4:
              # do the first few batches with max size to avoid later oom
              new_resize = upper_size
          else:
              new_resize = np.random.choice(
                  np.arange(lower_size, upper_size + 16, 16)
              )
          if new_resize != x.shape[2]:
              x = F.interpolate(x, size=new_resize, mode="bicubic")
          x = x.detach()
      return x
  
imports: import numpy
  import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from torch.optim.lr_scheduler import LambdaLR
  from ldm.modules.ema import LitEma
  from vqvae_quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.util import instantiate_from_config
  import ldm.models.autoencoder
  from packaging import version
label_desc: Use image node_id 10 for calling the VQModel obj's underlying member method code with example usage: obj.get_input(batch, k) and returns: x