class_docstr: Convert all TVTensors to pure tensors, removing associated metadata (if any).
  
  This doesn't scale or change the values, only the type.
class_docstr_tok: ["Convert","all","TVTensors","to","pure","tensors",",","removing","associated","metadata","(","if","any",")",".","This","does","n't","scale","or","change","the","values",",","only","the","type","."]
code: def _transform(
      self, inpt: Any, params: Dict[str, Any]
  ) -> torch.Tensor:
      return inpt.as_subclass(torch.Tensor)
  
file_path: vision/torchvision/transforms/v2/_type_conversion.py
imports: from typing import Any, Dict, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from torchvision import tv_tensors
  from torchvision.transforms.v2 import functional, Transform
  from torchvision.transforms.v2._utils import is_pure_tensor
label_desc: Use image node_id 1 for calling the ToPureTensor obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: inpt