code: def schedule_concatenate(outs):
      """Schedule for concatenate op.
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of concatenate 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])
      x = outs[0]
      tvm.te.schedule.AutoInlineInjective(s)
      if len(s[x].op.axis) >= 4:
          fused = s[x].fuse(
              s[x].op.axis[0], s[x].op.axis[1], s[x].op.axis[2]
          )
          s[x].parallel(fused)
      elif len(s[x].op.axis) >= 3:
          fused = s[x].fuse(s[x].op.axis[0], s[x].op.axis[1])
          s[x].parallel(fused)
      elif len(s[x].op.axis) >= 2:
          s[x].parallel(s[x].op.axis[0])
      return s
  
imports: import numpy
  import tvm
  from tvm import te
  from ..utils import is_empty_shape
label_desc: Use image node_id 3 for calling a global function with example usage: schedule_concatenate(outs) and returns: s