code: def schedule_conv2d_winograd_weight_transform(outs):
      """Schedule for weight transformation of winograd
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of this operator
            in the format of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      # Typically this is computed in PreCompute pass
      # so we make a schedule here for cpu llvm
      s = te.create_schedule([x.op for x in outs])
      output = outs[0]
      _, G = s[output].op.input_tensors
      s[G].compute_inline()
      eps, nu, co, ci = s[output].op.axis
      r_kh, r_kw = s[output].op.reduce_axis
      s[output].reorder(co, ci, r_kh, r_kw, eps, nu)
      for axis in [r_kh, r_kw, eps, nu]:
          s[output].unroll(axis)
      s[output].parallel(co)
      return s
  
imports: from tvm import te
  from .default import default_schedule
label_desc: Use image node_id 11 for calling a global function with example usage: schedule_conv2d_winograd_weight_transform(outs) and returns: s