class_docstr: A BatchNorm layer tracks multiple micro-batches to update running
  statistics per mini-batch.
docstr: Tracks statistics of a micro-batch.
docstr_tok: ["Tracks","statistics","of","a","micro-batch","."]
code: def _track(self, input: Tensor) -> bool:
      """Tracks statistics of a micro-batch."""
      # Dimensions except channel. For example, (0, 2, 3) is for BatchNorm2d.
      dim = [0]
      dim.extend(range(2, input.dim()))
  
      with torch.no_grad():
          self.sum += input.sum(dim)
          self.sum_squares += (input**2).sum(dim)
  
      size = input.size().numel() // input.size(1)
      self.counter += size
      self.tracked += 1
  
      return self.tracked == self.chunks
  
file_path: fairscale/fairscale/nn/pipe/batchnorm.py
imports: from typing import Optional, TypeVar, cast
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn.modules.batchnorm import _BatchNorm
  from .checkpoint import is_recomputing
label_desc: Use image node_id 3 for calling the DeferredBatchNorm obj's underlying member method code with example usage: obj._track(input) and returns: unknown