code: def __call__(
      self,
      y,
      H_split=True,
      explicit_nhwc=False,
      numSM=0,
      diagnostics=False,
  ):
      channels_last = (
          y.is_contiguous(memory_format=torch.channels_last)
          and not explicit_nhwc
      )
      if H_split:
          if explicit_nhwc:
              _, Hs, _, _ = list(y.shape)
              H = Hs - 2 * self.half_halo
              low_out_halo = y[
                  :, self.half_halo : 2 * self.half_halo, :, :
              ]
              low_tx = self._allocate_peer_tensor(low_out_halo)
              low_inp_halo = y[:, : self.half_halo, :, :]
              high_out_halo = y[:, H : H + self.half_halo, :, :]
              high_tx = self._allocate_peer_tensor(high_out_halo)
              high_inp_halo = y[
                  :, H + self.half_halo : H + 2 * self.half_halo, :, :
              ]
          else:
              _, _, Hs, _ = list(y.shape)
              H = Hs - 2 * self.half_halo
              low_out_halo = y[
                  :, :, self.half_halo : 2 * self.half_halo, :
              ]
              low_tx = self._allocate_peer_tensor(low_out_halo)
              low_inp_halo = y[:, :, : self.half_halo, :]
              high_out_halo = y[:, :, H : H + self.half_halo, :]
              high_tx = self._allocate_peer_tensor(high_out_halo)
              high_inp_halo = y[
                  :, :, H + self.half_halo : H + 2 * self.half_halo, :
              ]
      else:
          if explicit_nhwc:
              _, _, Ws, _ = list(y.shape)
              W = Ws - 2 * self.half_halo
              low_out_halo = y[
                  :, :, self.half_halo : 2 * self.half_halo, :
              ]
              low_tx = self._allocate_peer_tensor(low_out_halo)
              low_inp_halo = y[:, :, : self.half_halo, :]
              high_out_halo = y[:, :, W : W + self.half_halo, :]
              high_tx = self._allocate_peer_tensor(high_out_halo)
              high_inp_halo = y[
                  :, :, W + self.half_halo : W + 2 * self.half_halo, :
              ]
          else:
              _, _, _, Ws = list(y.shape)
              W = Ws - 2 * self.half_halo
              low_out_halo = y[
                  :, :, :, self.half_halo : 2 * self.half_halo
              ]
              low_tx = self._allocate_peer_tensor(low_out_halo)
              low_inp_halo = y[:, :, :, : self.half_halo]
              high_out_halo = y[:, :, :, W : W + self.half_halo]
              high_tx = self._allocate_peer_tensor(high_out_halo)
              high_inp_halo = y[
                  :, :, :, W + self.half_halo : W + 2 * self.half_halo
              ]
      pm.push_pull_halos_1d(
          diagnostics,
          explicit_nhwc,
          numSM,
          self.peer_rank,
          self.low_zero,
          low_out_halo,
          low_tx[self.peer_rank],
          high_tx[self.low_neighbor],
          low_inp_halo,
          self.high_zero,
          high_out_halo,
          high_tx[self.peer_rank],
          low_tx[self.high_neighbor],
          high_inp_halo,
      )
  
imports: import torch
  from apex.contrib.peer_memory import PeerMemoryPool
  import peer_memory_cuda
label_desc: Use image node_id 3 for calling the PeerHaloExchanger1d obj's underlying member method code with example usage: obj.__call__(y, H_split, explicit_nhwc, numSM, diagnostics) without return types