code: def batch_flatten_stir_schedule(
      out: te.Tensor,
      inp: te.Tensor,
      out_layout: str,
      in_layout: str,
  ) -> tir.Schedule:
      """STIR schedule definition for the compute of batch flatten compute.
      Parameters
      ----------
      outputs : te.Tensor
          The output tensor as returned by a call to batch_flatten_compute
      input : te.Tensor
          Input tensor to batch_flatten
      out_layout: typing.Callable
          The transformation function definition for the expected output layout
      in_layout: typing.Callable
          The transformation function definition for the input layout
      Returns
      -------
      sch : tvm.tir.Schedule
          The STIR schedule for slice batch flatten compute
      """
  
      batch_flatten_func = te.create_prim_func([inp, out])
      sch = tir.Schedule(batch_flatten_func, debug_mask="all")
      compute = sch.get_block("compute")
  
      sch.transform_layout(
          compute, inp.name, get_layout_transform_fn(in_layout)
      )
      sch.transform_layout(
          compute, out.name, get_layout_transform_fn(out_layout)
      )
      i, j = sch.get_loops(compute)
      jout, channel = sch.split(j, [None, inp.shape[3]])
      height, width = sch.split(jout, [inp.shape[1], inp.shape[2]])
      channelo, channeli = sch.split(channel, [None, 1024])
      channelio, channelii = sch.split(channeli, [None, 64])
      sch.reorder(i, height, width, channelo, channelio, channelii)
      sch.vectorize(channelii)
      return sch
  
imports: from tvm import te, tir, topi
  from ..utils import get_layout_transform_fn
label_desc: Use image node_id 2 for calling a global function with example usage: batch_flatten_stir_schedule(out, inp, out_layout, in_layout) and returns: sch