class_docstr: The :class:`BatchNorm1d` applies Batch Normalization over 2D/3D input (a mini-batch of 1D
  inputs (optional) with additional channel dimension), of shape (N, C) or (N, L, C) or (N, C, L).
  See more details in :class:`BatchNorm`.
  
  Examples
  ---------
  With TensorLayer
  
  >>> # in static model, no need to specify num_features
  >>> net = tl.layers.Input([None, 50, 32], name='input')
  >>> net = tl.layers.BatchNorm1d()(net)
  >>> # in dynamic model, build by specifying num_features
  >>> conv = tl.layers.Conv1d(32, 5, 1, in_channels=3)
  >>> bn = tl.layers.BatchNorm1d(num_features=32)
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code: def _check_input_shape(self, inputs):
      inputs_shape = _compute_shape(inputs)
      if len(inputs_shape) != 2 and len(inputs_shape) != 3:
          raise ValueError(
              "expected input to be 2D or 3D, but got {}D input".format(
                  inputs.ndim
              )
          )
  
imports: import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import moving_averages
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 1 for calling the BatchNorm1d obj's underlying member method code with example usage: obj._check_input_shape(inputs) without return types