code: def schedule_conv1d_nwc(outs):
      """Create schedule for tensors"""
      s = te.create_schedule([x.op for x in outs])
      output_op = outs[0].op
      scheduled_ops = []
  
      def traverse(op):
          """Traverse operators from computation graph"""
          # inline all one-to-one-mapping operators except the last stage (output)
          if tag.is_broadcast(op.tag):
              if op not in s.outputs:
                  s[op].compute_inline()
              else:  # inject custom schedule
                  if len(op.axis) == 3:  # schedule bias + bn + relu
                      n, w, c = op.axis
                      fused = s[op].fuse(n, w)
                      s[op].parallel(fused)
                      s[op].vectorize(c)
              for tensor in op.input_tensors:
                  if (
                      isinstance(tensor.op, te.tensor.ComputeOp)
                      and tensor.op not in scheduled_ops
                  ):
                      traverse(tensor.op)
  
          if "conv1d_nwc" in op.tag:
              conv = op.output(0)
              kernel = op.input_tensors[1]
              if (
                  isinstance(kernel.op, te.tensor.ComputeOp)
                  and "dilate" in kernel.op.tag
              ):
                  s[kernel].compute_inline()
  
              data = op.input_tensors[0]
              data_pad = None
              if (
                  isinstance(data.op, te.tensor.ComputeOp)
                  and "pad" in data.op.tag
              ):
                  data_pad = data
                  data = data_pad.op.input_tensors[0]
  
              n_pad, w_pad, c_pad = data_pad.op.axis
              pad_fused = s[data_pad].fuse(n_pad, w_pad)
              s[data_pad].parallel(pad_fused)
              C = conv
              n, w, c = C.op.axis
              rc, rw = C.op.reduce_axis
              n_out, w_out, c_out = output_op.axis
              s[C].vectorize(c)
              if op != output_op:  # fuse bias + bn + relu into conv
                  s[C].compute_at(s[output_op], c_out)
              else:
                  fused = s[C].fuse(n, w)
                  s[C].parallel(fused)
  
          scheduled_ops.append(op)
  
      traverse(output_op)
      return s
  
imports: from tvm import te
  from ..None import tag
label_desc: Use image node_id 2 for calling a global function with example usage: schedule_conv1d_nwc(outs) and returns: s