code: def forward(self, input: Tensor) -> Tensor:
      self._check_input_dim(input)
  
      # exponential_average_factor is set to self.momentum
      # (when it is available) only so that it gets updated
      # in ONNX graph when this node is exported to ONNX.
      if self.momentum is None:
          exponential_average_factor = 0.0
      else:
          exponential_average_factor = self.momentum
  
      if self.training and self.track_running_stats:
          # TODO: if statement only here to tell the jit to skip emitting this when it is None
          if self.num_batches_tracked is not None:  # type: ignore[has-type]
              self.num_batches_tracked.add_(1)  # type: ignore[has-type]
              if self.momentum is None:  # use cumulative moving average
                  exponential_average_factor = 1.0 / float(
                      self.num_batches_tracked
                  )
              else:  # use exponential moving average
                  exponential_average_factor = self.momentum
  
      r"""
          Decide whether the mini-batch stats should be used for normalization rather than the buffers.
          Mini-batch stats are used in training mode, and in eval mode when buffers are None.
          """
      if self.training:
          bn_training = True
      else:
          bn_training = (self.running_mean is None) and (
              self.running_var is None
          )
  
      r"""
          Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
          passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
          used for normalization (i.e. in eval mode when buffers are not None).
          """
      return F.batch_norm(
          input,
          # If buffers are not to be tracked, ensure that they won't be updated
          self.running_mean
          if not self.training or self.track_running_stats
          else None,
          self.running_var
          if not self.training or self.track_running_stats
          else None,
          self.weight,
          self.bias,
          bn_training,
          exponential_average_factor,
          self.eps,
      )
  
imports: from typing import Optional, Any
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter, UninitializedParameter, UninitializedBuffer
  from ..None import functional
  from ..None import init
  from ._functions import SyncBatchNorm
  from .lazy import LazyModuleMixin
  from .module import Module
label_desc: Use image node_id 2 for calling the _BatchNorm obj's underlying member method code with example usage: obj.forward(input) and returns: F