class_docstr: Convert tensor data type from uint8 to float, divide value by 255.0 and
  permute the dimensions of clip tensor
class_docstr_tok: ["Convert","tensor","data","type","from","uint8","to","float",",","divide","value","by","255.0","and","permute","the","dimensions","of","clip","tensor"]
docstr: Args:
      clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
  Return:
      clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
docstr_tok: ["Args",":","clip","(","torch.tensor",",","dtype=torch.uint8",")",":","Size","is","(","T",",","H",",","W",",","C",")","Return",":","clip","(","torch.tensor",",","dtype=torch.float",")",":","Size","is","(","C",",","T",",","H",",","W",")"]
code: def __call__(self, clip):
      """
      Args:
          clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
      Return:
          clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
      """
      return F.to_tensor(clip)
  
imports: import numbers
  import random
  from torchvision.transforms import RandomCrop, RandomResizedCrop
  import lavis.processors.functional_video
label_desc: Use image node_id 2 for calling the ToTensorVideo obj's underlying member method code with example usage: obj.__call__(clip) and returns: F