def METHOD_NAME(outs, layout):
    """Schedule for pool
    Parameters
    ----------
    outs: Array of Tensor
          The computation graph description of pool
          in the format of an array of tensors.
    layout: str
        Data layout.
    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])
    scheduled_ops = []
    def _schedule(PaddedInput, Pool):
        if isinstance(PaddedInput.op, te.tensor.ComputeOp):
            s[PaddedInput].compute_inline()
        do_vectorize = layout[-1] not in "DHWdhw"
        _parallel_sch(s[Pool], outs[0].shape, do_vectorize)
    def traverse(OP):
        """Internal traverse function"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_injective(OP.tag):
            if OP not in s.outputs:
                s[OP].compute_inline()
            for tensor in OP.input_tensors:
                if isinstance(tensor.op, te.tensor.ComputeOp) and tensor.op not in scheduled_ops:
                    traverse(tensor.op)
        # schedule pool
        elif OP.tag.startswith("pool"):
            # Average pool accumulation and division happens in different for loops (#3607).
            # To ensure good parallel support, apply multi-threading on the second loop.
            if OP != outs[0].op:
                output = outs[0]
                output_fused = s[output].fuse(output.op.axis[0], output.op.axis[1])
                s[output].parallel(output_fused)
            PaddedInput = OP.input_tensors[0]
            Pool = OP.output(0)
            _schedule(PaddedInput, Pool)
        else:
            raise RuntimeError(f"Unsupported operator: {OP.tag}")
        scheduled_ops.append(OP)
    traverse(outs[0].op)
    return s