def METHOD_NAME(sch, out):
    """Schedule for injective op from existing schedule.
    Parameters
    ----------
    sch: Schedule
         The schedule to update.
    out: Tensor
         The tensor representing the injective op.
    Returns
    -------
    sch: Schedule
         The updated schedule.
    """
    def find_nearest_small_factor(num, target):
        """Find the nearest factor of the given number that is smaller than the target."""
        for i in range(target, 0, -1):
            if num % i == 0:
                return i
        # Unreachable because i=1 must hold.
        return -1
    fused = sch[out].fuse(*sch[out].op.axis)
    num_thread = tvm.target.Target.current(allow_none=False).max_num_threads
    max_block = 256
    # Vectorize on fp16 data type to enable half2 for better memory bandwidth utilization.
    vector_width = 2 if out.dtype == "float16" else 1
    is_dynamic_output = False
    for dim in out.shape:
        if not isinstance(dim, tvm.tir.IntImm):
            is_dynamic_output = True
            break
    out_len = utils.prod(out.shape)
    try:
        const_size = utils.get_const_int(out_len)
        # Adjust block and thread to make sure they are dividable so that vectorize can be
        # correctly applied.
        if vector_width > 1 and const_size % vector_width == 0:
            remain_total_size = const_size // vector_width
            cand_sizes = []
            for max_size in [num_thread, max_block]:
                cand_sizes.append(
                    max_size
                    if remain_total_size % max_size == 0
                    else find_nearest_small_factor(remain_total_size, max_size)
                )
                remain_total_size //= cand_sizes[-1]
            # If the product of candidate dividable (block * thread) is too small,
            # then the performance may be worse even half2 is enabled. Note that 0.7
            # is just a heuristic ratio and may not be optimal for all workloads.
            if np.prod(cand_sizes) / (max_block * num_thread) >= 0.7:
                num_thread, max_block = cand_sizes
        need_block_split = const_size > max_block * num_thread * vector_width
    except ValueError:
        need_block_split = False
        const_size = 0
    if vector_width > 1:
        fused, v = sch[out].split(fused, vector_width)
        sch[out].vectorize(v)
    if need_block_split:
        xo, xi = sch[out].split(fused, factor=num_thread * max_block)
        bx, tx = sch[out].split(xi, factor=num_thread)
        sch[out].reorder(bx, tx, xo)
        sch[out].bind(bx, te.thread_axis("blockIdx.x"))
        sch[out].bind(tx, te.thread_axis("threadIdx.x"))
    else:
        # Use less threads for dynamic shape ops to avoid runtime error.
        if is_dynamic_output:
            num_thread //= 2
        if const_size != 0 and const_size < num_thread:
            bx, tx = sch[out].split(fused, factor=const_size)
        else:
            bx, tx = sch[out].split(fused, factor=num_thread)
        sch[out].bind(tx, te.thread_axis("threadIdx.x"))
        sch[out].bind(bx, te.thread_axis("blockIdx.x"))
    return sch