code: def transform_loc_ir(
      loc_pred,
      anchor,
      temp_valid_count,
      temp_cls_id,
      temp_score,
      out,
      clip,
      variances,
      batch_size,
      num_anchors,
      keep_background,
  ):
      """Low level IR routing for transform location in multibox_detection operator.
  
      Parameters
      ----------
      loc_pred : Buffer
          Buffer of location regression predictions.
  
      anchor : Buffer
          Buffer of prior anchor boxes.
  
      temp_valid_count : Buffer
          Intermediate result buffer.
  
      temp_cls_id : Buffer
          Intermediate result buffer.
  
      temp_score : Buffer
          Input buffer which stores intermediate results.
  
      out : Buffer
          Output buffer.
  
      clip : boolean
          Whether to clip out-of-boundary boxes.
  
      variances : tuple of float
          Variances to be decoded from box regression output.
  
      batch_size : int
          Batch size
  
      num_anchors : int
          Number of anchors
  
      keep_background : int
          1 to keep background, 0 to remove it.
  
      Returns
      -------
      stmt : Stmt
          The result IR statement.
      """
  
      def transform_loc(
          loc,
          loc_base_idx,
          anchor,
          anchor_base_idx,
          clip,
          vx,
          vy,
          vw,
          vh,
      ):
          """Transform prior anchor box to output box through location predictions."""
          al = anchor[anchor_base_idx]
          at = anchor[anchor_base_idx + 1]
          ar = anchor[anchor_base_idx + 2]
          ab = anchor[anchor_base_idx + 3]
          aw = ar - al
          ah = ab - at
          ax = (al + ar) / 2.0
          ay = (at + ab) / 2.0
          px = loc[loc_base_idx]
          py = loc[loc_base_idx + 1]
          pw = loc[loc_base_idx + 2]
          ph = loc[loc_base_idx + 3]
          ox = px * vx * aw + ax
          oy = py * vy * ah + ay
          ow = exp(pw * vw) * aw / 2.0
          oh = exp(ph * vh) * ah / 2.0
          return (
              tvm.tir.if_then_else(
                  clip,
                  tvm.te.max(0.0, tvm.te.min(1.0, ox - ow)),
                  ox - ow,
              ),
              tvm.tir.if_then_else(
                  clip,
                  tvm.te.max(0.0, tvm.te.min(1.0, oy - oh)),
                  oy - oh,
              ),
              tvm.tir.if_then_else(
                  clip,
                  tvm.te.max(0.0, tvm.te.min(1.0, ox + ow)),
                  ox + ow,
              ),
              tvm.tir.if_then_else(
                  clip,
                  tvm.te.max(0.0, tvm.te.min(1.0, oy + oh)),
                  oy + oh,
              ),
          )
  
      ib = tvm.tir.ir_builder.create()
  
      loc_pred = ib.buffer_ptr(loc_pred)
      anchor = ib.buffer_ptr(anchor)
      temp_valid_count = ib.buffer_ptr(temp_valid_count)
      cls_id = ib.buffer_ptr(temp_cls_id)
      score = ib.buffer_ptr(temp_score)
      out_loc = ib.buffer_ptr(out)
  
      keep_background = tvm.tir.IntImm("int8", keep_background)
  
      max_threads = int(
          tvm.target.Target.current(allow_none=False).max_num_threads
      )
      nthread_tx = max_threads
      nthread_bx = (batch_size * num_anchors) // max_threads + 1
      tx = te.thread_axis("threadIdx.x")
      bx = te.thread_axis("blockIdx.x")
      ib.scope_attr(tx, "thread_extent", nthread_tx)
      ib.scope_attr(bx, "thread_extent", nthread_bx)
      tid = bx * max_threads + tx
  
      idxd = tvm.tir.indexdiv
      idxm = tvm.tir.indexmod
  
      with ib.if_scope(tid < batch_size * num_anchors):
          i = idxd(tid, num_anchors)
          j = idxm(tid, num_anchors)
  
          with ib.if_scope(
              tvm.tir.any(keep_background == 1, cls_id[tid] > 0)
          ):
              with ib.if_scope(j == 0):
                  out_base_idx = i * num_anchors * 6
                  out_loc[out_base_idx] = (
                      cls_id[tid] * 1.0
                      if keep_background == 1
                      else cls_id[tid] - 1.0
                  )
                  out_loc[out_base_idx + 1] = score[tid]
                  (
                      out_loc[out_base_idx + 2],
                      out_loc[out_base_idx + 3],
                      out_loc[out_base_idx + 4],
                      out_loc[out_base_idx + 5],
                  ) = transform_loc(
                      loc_pred,
                      tid * 4,
                      anchor,
                      j * 4,
                      clip,
                      variances[0],
                      variances[1],
                      variances[2],
                      variances[3],
                  )
              with ib.else_scope():
                  out_base_idx = (
                      i * num_anchors * 6
                      + temp_valid_count[tid - 1] * 6
                  )
                  out_loc[out_base_idx] = (
                      cls_id[tid] * 1.0
                      if keep_background == 1
                      else cls_id[tid] - 1.0
                  )
                  out_loc[out_base_idx + 1] = score[tid]
                  (
                      out_loc[out_base_idx + 2],
                      out_loc[out_base_idx + 3],
                      out_loc[out_base_idx + 4],
                      out_loc[out_base_idx + 5],
                  ) = transform_loc(
                      loc_pred,
                      tid * 4,
                      anchor,
                      j * 4,
                      clip,
                      variances[0],
                      variances[1],
                      variances[2],
                      variances[3],
                  )
  
      return ib.get()
  
file_path: tvm/python/tvm/topi/cuda/ssd/multibox.py
imports: import math
  import tvm
  from tvm import te
  from tvm.tir import if_then_else, exp
  from tvm import topi
  from ..nms import non_max_suppression
label_desc: Use image node_id 4 for calling a global function with example usage: transform_loc_ir(loc_pred, anchor, temp_valid_count, temp_cls_id, temp_score, out, clip, variances, batch_size, num_anchors, keep_background) and returns: ib, tvm,  tvm,  tvm,  tvm