code: def forward(self, inputs, states):
      i2h, h2h = self._conv_forward(inputs, states)
      gates = i2h + h2h
      slice_gates = npx.slice_channel(
          gates, num_outputs=4, axis=self._channel_axis
      )
      in_gate = npx.activation(slice_gates[0], act_type="sigmoid")
      forget_gate = npx.activation(slice_gates[1], act_type="sigmoid")
      in_transform = self._get_activation(
          slice_gates[2], self._activation
      )
      out_gate = npx.activation(slice_gates[3], act_type="sigmoid")
      next_c = (
          forget_gate * states[1].to_device(inputs.device)
          + in_gate * in_transform
      )
      next_h = np.multiply(
          out_gate, self._get_activation(next_c, self._activation)
      )
  
      return next_h, [next_h, next_c]
  
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 _ConvLSTMCell obj's underlying member method code with example usage: obj.forward(inputs, states) and returns: next_h,  list