class_docstr: Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255]
  to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]
class_docstr_tok: ["Convert","a","list","of","m","(","H","x","W","x","C",")","numpy.ndarrays","in","the","range","[","0",",","255","]","to","a","torch.FloatTensor","of","shape","(","C","x","m","x","H","x","W",")","in","the","range","[","0",",","1.0","]"]
code: def __init__(self, channel_nb=3, div_255=True, numpy=False):
      self.channel_nb = channel_nb
      self.div_255 = div_255
      self.numpy = numpy
  
file_path: gluon-cv/gluoncv/torch/data/transforms/videotransforms/volume_transforms.py
imports: import numpy
  from PIL import Image
  import torch
  from .utils import images
label_desc: Use image node_id 1 to create a new ClipToTensor_K object from inherited base classes: object with example: obj = ClipToTensor_K(channel_nb, div_255, numpy)