code: def lrn(data, size, axis=1, alpha=0.0001, beta=0.75, bias=2):
      """Perform the across channels local response normalisation
      on the input data.
  
      sum_sqr_up^i{x, y} = (bias+((alpha/size)* \
                                  {sum_{j=max(0, i-size/2)}^{min(N-1,i+size/2)} \
                                       (data^j{x,y})^2}))^beta
      output^i{x, y} = data^i{x, y}/sum_sqr_up^i{x, y}
      N is the number for input channels
  
      Parameters
      ----------
      data : tvm.te.Tensor
          4-D with shape [batch, channel, height, width]
  
      size : int
          normalisation window size
  
      axis : int
          input data layout channel axis
          default value is 1 for NCHW format
  
      bias : float
          offset to avoid dividing by 0
  
      alpha : float
          to be divided
  
      beta : float
          exponent
  
      Returns
      -------
      output : tvm.te.Tensor
          4-D output with same shape
      """
      return cpp.nn.lrn(data, size, axis, alpha, beta, bias)
  
label_desc: Use image node_id 1 for calling a global function with example usage: lrn(data, size, axis, alpha, beta, bias) and returns: cpp