def METHOD_NAME(outs):
    """X86 schedule for concatenate op.
    Parameters
    ----------
    outs: Array of Tensor
          The computation graph description of injective in the format
          of an array of tensors.
    Returns
    -------
    sch: Schedule
        The computation schedule for the op.
    """
    def vectorize(sch, tensor, vectorize_limit):
        """Internal vectorization function for concatenate."""
        inner_axis = s[tensor].op.axis[len(s[tensor].op.axis) - 1]
        # Check that the tensor shape is static. Otherwise skip vectorization.
        if isinstance(tensor.shape[len(tensor.shape) - 1], IntImm):
            inner_length = tensor.shape[len(tensor.shape) - 1].value
            if inner_length <= vectorize_limit:
                sch[tensor].vectorize(inner_axis)
            else:
                split_factor = 1
                for i in range(vectorize_limit, 1, -1):
                    if inner_length % i == 0:
                        split_factor = i
                        break
                if split_factor > 1:
                    _, inner_i = sch[tensor].split(inner_axis, split_factor)
                    sch[tensor].vectorize(inner_i)
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    x = outs[0]
    s = te.create_schedule([x.op for x in outs])
    te.schedule.AutoInlineInjective(s)
    if len(s[x].op.axis) >= 5:
        fused = s[x].fuse(s[x].op.axis[0], s[x].op.axis[1], s[x].op.axis[2])
        vectorize(s, x, 64)
        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)
    else:
        s[x].parallel(s[x].op.axis[0])
    return s