class_docstr: A :class:`torch.nn.BatchNorm1d` module with lazy initialization.
  
  Lazy initialization based on the ``num_features`` argument of the :class:`BatchNorm1d` that is inferred
  from the ``input.size(1)``.
  The attributes that will be lazily initialized are `weight`, `bias`,
  `running_mean` and `running_var`.
  
  Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
  on lazy modules and their limitations.
  
  Args:
      eps: a value added to the denominator for numerical stability.
          Default: 1e-5
      momentum: the value used for the running_mean and running_var
          computation. Can be set to ``None`` for cumulative moving average
          (i.e. simple average). Default: 0.1
      affine: a boolean value that when set to ``True``, this module has
          learnable affine parameters. Default: ``True``
      track_running_stats: a boolean value that when set to ``True``, this
          module tracks the running mean and variance, and when set to ``False``,
          this module does not track such statistics, and initializes statistics
          buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
          When these buffers are ``None``, this module always uses batch statistics.
          in both training and eval modes. Default: ``True``
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code: def _check_input_dim(self, input):
      if input.dim() != 2 and input.dim() != 3:
          raise ValueError(
              f"expected 2D or 3D input (got {input.dim()}D input)"
          )
  
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 1 for calling the LazyBatchNorm1d obj's underlying member method code with example usage: obj._check_input_dim(input) without return types