class_docstr: Removes empty dimensions from the mask and converts to a torch.float32 tensor.
  Typically used with B/W masks to remove the "channel" dimension
  
  :return tensor
class_docstr_tok: ["Removes","empty","dimensions","from","the","mask","and","converts","to","a","torch.float32","tensor",".","Typically","used","with","B\/W","masks","to","remove","the","``","channel","''","dimension",":","return","tensor"]
code: def __call__(self, img):
      # Note, we cannot call the normal torchvision to_tensor method here because it automatically divides by 255 which is NOT what we want.
      return self.to_tensor(img).squeeze()
  
imports: import random
  import torch
  from torchvision.transforms.functional import to_tensor
  import numpy
  from ..utils import th_random_choice
label_desc: Use image node_id 2 for calling the MaskToSqueezedTensor obj's underlying member method code with example usage: obj.__call__(img) and returns: self