class_docstr: Abstract base class for convolutional RNNs
class_docstr_tok: ["Abstract","base","class","for","convolutional","RNNs"]
code: def infer_shape(self, i, x, is_bidirect):
      channel_axis = self._conv_layout.find("C")
      shape_c = x.shape[-len(self._i2h_kernel) - 1 :][channel_axis - 1]
      wshape = self.i2h_weight.shape
      wshape_list = list(wshape)
      wshape_list[self._conv_layout.find("C")] = shape_c
      self.i2h_weight.shape = tuple(wshape_list)
  
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 8 for calling the _BaseConvRNNCell obj's underlying member method code with example usage: obj.infer_shape(i, x, is_bidirect) without return types