class_docstr: Applies 1d batch normalization to the input tensor.
  
  Arguments
  ---------
  input_shape : tuple
      The expected shape of the input. Alternatively, use ``input_size``.
  input_size : int
      The expected size of the input. Alternatively, use ``input_shape``.
  eps : float
      This value is added to std deviation estimation to improve the numerical
      stability.
  momentum : float
      It is a value used for the running_mean and running_var computation.
  affine : bool
      When set to True, the affine parameters are learned.
  track_running_stats : bool
      When set to True, this module tracks the running mean and variance,
      and when set to False, this module does not track such statistics.
  combine_batch_time : bool
      When true, it combines batch an time axis.
  
  
  Example
  -------
  >>> input = torch.randn(100, 10)
  >>> norm = BatchNorm1d(input_shape=input.shape)
  >>> output = norm(input)
  >>> output.shape
  torch.Size([100, 10])
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code: def __init__(
      self,
      input_shape=None,
      input_size=None,
      eps=1e-05,
      momentum=0.1,
      affine=True,
      track_running_stats=True,
      combine_batch_time=False,
      skip_transpose=False,
  ):
      super().__init__()
      self.combine_batch_time = combine_batch_time
      self.skip_transpose = skip_transpose
  
      if input_size is None and skip_transpose:
          input_size = input_shape[1]
      elif input_size is None:
          input_size = input_shape[-1]
  
      self.norm = nn.BatchNorm1d(
          input_size,
          eps=eps,
          momentum=momentum,
          affine=affine,
          track_running_stats=track_running_stats,
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new BatchNorm1d object from inherited base classes: nn with example: obj = BatchNorm1d(input_shape, input_size, eps, momentum, affine, track_running_stats, combine_batch_time, skip_transpose)