parameters: (inputs, local_size=5, alpha=0.0001, beta=0.75, k=2.0,
          mode='ACROSS_CHANNELS', data_format='NCHW', **kwargs)
return_statement: return output
docstring: Local Response Normalization, introduced by `[Krizhevsky et.al, 2012] <http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks>`_.
  
      Parameters
      ----------
      inputs : Tensor
          The input tensor.
      local_size : int
          The local size of LRN.
      alpha : float
          The alpha of LRN.
      beta : float
          The beta of LRN.
      k : float
          The k of LRN.
      mode : str
          The mode, ``ACROSS_CHANNELS`` or ``WITHIN_CHANNEL``.
      data_format : str
          The data format. ``NCHW`` or ``NHWC``.
  
      Returns
      -------
      Tensor
          The output tensor.
docstring_summary: Local Response Normalization, introduced by `[Krizhevsky et.al, 2012] <http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks>`_.
docstring_tokens: Local Response Normalization introduced by [ Krizhevsky et . al 2012 ] <http : // papers . nips . cc / paper / 4824 - imagenet - classification - with - deep - convolutional - neural - networks > _ .
function: def LRN(inputs, local_size=5, alpha=0.0001, beta=0.75, k=2.0,
          mode='ACROSS_CHANNELS', data_format='NCHW', **kwargs):
      """Local Response Normalization, introduced by `[Krizhevsky et.al, 2012] <http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks>`_.
  
      Parameters
      ----------
      inputs : Tensor
          The input tensor.
      local_size : int
          The local size of LRN.
      alpha : float
          The alpha of LRN.
      beta : float
          The beta of LRN.
      k : float
          The k of LRN.
      mode : str
          The mode, ``ACROSS_CHANNELS`` or ``WITHIN_CHANNEL``.
      data_format : str
          The data format. ``NCHW`` or ``NHWC``.
  
      Returns
      -------
      Tensor
          The output tensor.
  
      """
      CheckInputs(inputs, 1)
      arguments = ParseArguments(locals())
  
      if mode not in ('ACROSS_CHANNELS', 'WITHIN_CHANNEL'):
          raise ValueError('Unsupported lrn mode: {}'.format(mode))
      if data_format not in ('NCHW', 'NHWC'):
          raise ValueError('Unsupported data format: {}'.format(data_format))
  
      output = Tensor.CreateOperator(nout=1, op_type='LRN', **arguments)
  
      if inputs.shape is not None:
          output.shape = inputs.shape[:]
  
      return output
function_tokens: def LRN ( inputs , local_size = 5 , alpha = 0.0001 , beta = 0.75 , k = 2.0 , mode = 'ACROSS_CHANNELS' , data_format = 'NCHW' , * * kwargs ) : CheckInputs ( inputs , 1 ) arguments = ParseArguments ( locals ( ) ) if mode not in ( 'ACROSS_CHANNELS' , 'WITHIN_CHANNEL' ) : raise ValueError ( 'Unsupported lrn mode: {}' . format ( mode ) ) if data_format not in ( 'NCHW' , 'NHWC' ) : raise ValueError ( 'Unsupported data format: {}' . format ( data_format ) ) output = Tensor . CreateOperator ( nout = 1 , op_type = 'LRN' , * * arguments ) if inputs . shape is not None : output . shape = inputs . shape [ : ] return output