code: def _cast_squeeze_in(
      img: Tensor, req_dtypes: List[torch.dtype]
  ) -> Tuple[Tensor, bool, bool, torch.dtype]:
      need_squeeze = False
      # make image NCHW
      if img.ndim < 4:
          img = img.unsqueeze(dim=0)
          need_squeeze = True
  
      out_dtype = img.dtype
      need_cast = False
      if out_dtype not in req_dtypes:
          need_cast = True
          req_dtype = req_dtypes[0]
          img = img.to(req_dtype)
      return img, need_cast, need_squeeze, out_dtype
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn.functional import conv2d, grid_sample, interpolate, pad
label_desc: Use image node_id 26 for calling a global function with example usage: _cast_squeeze_in(img, req_dtypes) and returns: img,  need_cast,  need_squeeze,  out_dtype