class_docstr: Converts a PIL, numpy or CV image to a torch.float32 representation
class_docstr_tok: ["Converts","a","PIL",",","numpy","or","CV","image","to","a","torch.float32","representation"]
docstr: :param divisor: Optional divisor for the conversion. Can be specified to convert supplied images from [0-255] range to [0.0-1.0]
code: def __init__(self, divisor: float = None):
      """
      :param divisor: Optional divisor for the conversion. Can be specified to convert supplied images from [0-255] range to [0.0-1.0]
      """
      self.divisor = divisor
  
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 1 to create a new MaskToFloatTensor object  with example: obj = MaskToFloatTensor(divisor)