code: def forward(self, inputs, states):
      i2h, h2h = self._conv_forward(inputs, states)
  
      i2h_r, i2h_z, i2h = npx.slice_channel(
          i2h, num_outputs=3, axis=self._channel_axis
      )
      h2h_r, h2h_z, h2h = npx.slice_channel(
          h2h, num_outputs=3, axis=self._channel_axis
      )
  
      reset_gate = npx.activation(i2h_r + h2h_r, act_type="sigmoid")
      update_gate = npx.activation(i2h_z + h2h_z, act_type="sigmoid")
  
      next_h_tmp = self._get_activation(
          i2h + reset_gate * h2h, self._activation
      )
  
      next_h = (1.0 - update_gate) * next_h_tmp + update_gate * states[
          0
      ].to_device(inputs.device)
  
      return next_h, [next_h]
  
imports: from math import floor
  from ...base import numeric_types
  from .rnn_cell import HybridRecurrentCell
  from ..parameter import Parameter
  from ...None import np, npx
  from ...util import use_np
label_desc: Use image node_id 5 for calling the _ConvGRUCell obj's underlying member method code with example usage: obj.forward(inputs, states) and returns: next_h,  list