class_docstr: Layer that performs batch normalization.
  
  In training, batch normalization keeps smoothed cumulative statistics across
  batches of input data and modifies each new batch so that its components are
  normally distributed. In eval or inference, a `BatchNorm` instance uses its
  stored mean and variance to approximately normalize each new batch of data.
  
  See https://arxiv.org/abs/1502.03167 for original presentation and motivation
  of batch normalization).
class_docstr_tok: ["Layer","that","performs","batch","normalization",".","In","training",",","batch","normalization","keeps","smoothed","cumulative","statistics","across","batches","of","input","data","and","modifies","each","new","batch","so","that","its","components","are","normally","distributed",".","In","eval","or","inference",",","a","`","BatchNorm","`","instance","uses","its","stored","mean","and","variance","to","approximately","normalize","each","new","batch","of","data",".","See","https",":","\/\/arxiv.org\/abs\/1502.03167","for","original","presentation","and","motivation","of","batch","normalization",")","."]
code: def __init__(
      self,
      axis=(0, 1, 2),
      epsilon=1e-5,
      center=True,
      scale=True,
      momentum=0.999,
      mode="train",
  ):
      super().__init__()
      self._axis = axis
      self._epsilon = epsilon
      self._center = center
      self._scale = scale
      self._momentum = momentum
      self._mode = mode
  
imports: from trax.fastmath import numpy
  from trax.layers import base
label_desc: Use image node_id 1 to create a new BatchNorm object from inherited base classes: base with example: obj = BatchNorm(axis, epsilon, center, scale, momentum, mode)