code: def schedule_lrn(outs):
      """Schedule for LRN
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of LRN
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
      s = te.create_schedule([x.op for x in outs])
      max_threads = int(
          tvm.target.Target.current(allow_none=False).max_num_threads
      )
  
      def _callback(op):
          if "sqr_sum" in op.tag:
              pad = op.input_tensors[0]
              s[pad].compute_inline()
              fused_axis = s[outs[0]].fuse(*s[outs[0]].op.axis)
              bx, tx = s[outs[0]].split(fused_axis, factor=max_threads)
              s[outs[0]].bind(bx, te.thread_axis("blockIdx.x"))
              s[outs[0]].bind(tx, te.thread_axis("threadIdx.x"))
              s[op].compute_at(s[outs[0]], tx)
  
      traverse_inline(s, outs[0].op, _callback)
      return s
  
imports: from __future__ import absolute_import
  import tvm
  from tvm import te
  from ..utils import traverse_inline
label_desc: Use image node_id 1 for calling a global function with example usage: schedule_lrn(outs) and returns: s