class_docstr: Applies batch normalization over a batch of features as described in
  the `"Batch Normalization: Accelerating Deep Network Training by
  Reducing Internal Covariate Shift" <https://arxiv.org/abs/1502.03167>`_
  paper.
  
  .. math::
      \mathbf{x}^{\prime}_i = \frac{\mathbf{x} -
      \textrm{E}[\mathbf{x}]}{\sqrt{\textrm{Var}[\mathbf{x}] + \epsilon}}
      \odot \gamma + \beta
  
  The mean and standard-deviation are calculated per-dimension over all nodes
  inside the mini-batch.
  
  Args:
      in_channels (int): Size of each input sample.
      eps (float, optional): A value added to the denominator for numerical
          stability. (default: :obj:`1e-5`)
      momentum (float, optional): The value used for the running mean and
          running variance computation. (default: :obj:`0.1`)
      affine (bool, optional): If set to :obj:`True`, this module has
          learnable affine parameters :math:`\gamma` and :math:`\beta`.
          (default: :obj:`True`)
      track_running_stats (bool, optional): If set to :obj:`True`, this
          module tracks the running mean and variance, and when set to
          :obj:`False`, this module does not track such statistics and always
          uses batch statistics in both training and eval modes.
          (default: :obj:`True`)
      allow_single_element (bool, optional): If set to :obj:`True`, batches
          with only a single element will work as during in evaluation.
          That is the running mean and variance will be used.
          Requires :obj:`track_running_stats=True`. (default: :obj:`False`)
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code: def __repr__(self):
      return f"{self.__class__.__name__}({self.module.num_features})"
  
file_path: pytorch_geometric/torch_geometric/nn/norm/batch_norm.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch.nn import Parameter
  from torch_geometric.nn.aggr.fused import FusedAggregation
label_desc: Use image node_id 5 for calling the BatchNorm obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str