code: def _skip_rnn_layer(self, F, x: Tensor) -> Tensor:
      skip_c = F.slice_axis(
          x, axis=2, begin=-self.channel_skip_count, end=None  # NCT
      )
      skip_c = F.reshape(
          skip_c, shape=(0, 0, -1, self.skip_size)
      )  # NCTxskip
      skip_c = F.transpose(skip_c, axes=(2, 0, 3, 1))  # TNxskipxC
      skip_c = F.reshape(
          skip_c, shape=(self.conv_skip, -1, self.channels)
      )  # T(Nxskip)C
      if F is mx.ndarray:
          ctx = (
              skip_c.context
              if isinstance(skip_c, mx.gluon.tensor_types)
              else skip_c[0].context
          )
          with ctx:
              begin_state = self.skip_rnn.begin_state(
                  func=F.zeros,
                  dtype=self.dtype,
                  batch_size=skip_c.shape[1],
              )
      else:
          begin_state = self.skip_rnn.begin_state(
              func=F.zeros, dtype=self.dtype, batch_size=0
          )
  
      s, _ = self.skip_rnn.unroll(
          inputs=skip_c,
          length=min(self.conv_skip, self.context_length),
          layout="TNC",
          merge_outputs=True,
          begin_state=begin_state,
      )
      s = F.squeeze(
          F.slice_axis(s, axis=0, begin=-1, end=None), axis=0
      )  # (Nxskip)xC
      s = F.reshape(
          s, shape=(-1, self.skip_size * self.skip_rnn_num_cells)
      )  # Nx(skipxC)
      return s
  
imports: from typing import Optional, Type
  import mxnet
  from mxnet.gluon import loss, nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
label_desc: Use image node_id 3 for calling the LSTNetBase obj's underlying member method code with example usage: obj._skip_rnn_layer(F, x) and returns: s