class_docstr: Tracks saved skip tensors through portals. The skip tensors will be
  hidden in portals so that the autograd engine does not need to track them.
  
  This tracker is only used when the training or evaluating module is wrapped
  with :class:`torchpipe.Pipe`.
class_docstr_tok: ["Tracks","saved","skip","tensors","through","portals",".","The","skip","tensors","will","be","hidden","in","portals","so","that","the","autograd","engine","does","not","need","to","track","them",".","This","tracker","is","only","used","when","the","training","or","evaluating","module","is","wrapped","with",":","class",":","`","torchpipe.Pipe","`","."]
docstr: Loads a skip tensor from the corresponding portal to pop. The given
  micro-batch is connected to the portal with :class:`Fork`.
docstr_tok: ["Loads","a","skip","tensor","from","the","corresponding","portal","to","pop",".","The","given","micro-batch","is","connected","to","the","portal","with",":","class",":","`","Fork","`","."]
code: def load(
      self, batch: Batch, ns: Namespace, name: str
  ) -> Optional[Tensor]:
      """Loads a skip tensor from the corresponding portal to pop. The given
      micro-batch is connected to the portal with :class:`Fork`.
      """
      if not self.skip_layout.requires_copy(ns, name):
          tensor = super().load(batch, ns, name)
          return tensor
  
      portal = self.portals[(ns, name)]
      tensor_idx = batch.find_tensor_idx()
      batch[tensor_idx], phony = fork(batch[tensor_idx])
      tensor = portal.orange(phony)
      return tensor
  
imports: from contextlib import contextmanager
  import threading
  from typing import Dict, Generator, List, Optional, Tuple
  from torch import Tensor
  from ..checkpoint import is_checkpointing
  from ..dependency import fork, join
  from ..microbatch import Batch
  from ..stream import AbstractStream
  from .layout import SkipLayout
  from .namespace import Namespace
  from .portal import Portal
label_desc: Use image node_id 3 for calling the SkipTrackerThroughPotals obj's underlying member method code with example usage: obj.load(batch, ns, name) and returns: tensor, tensor