class_docstr: Abstract base class for convolutional RNNs
class_docstr_tok: ["Abstract","base","class","for","convolutional","RNNs"]
code: def __repr__(self):
      s = "{name}({mapping}"
      if hasattr(self, "_activation"):
          s += ", {_activation}"
      s += ", {_conv_layout}"
      s += ")"
      attrs = self.__dict__
      shape = self.i2h_weight.shape
      in_channels = shape[1 if self._channel_axis == 1 else -1]
      mapping = "{0} -> {1}".format(
          in_channels if in_channels else None, shape[0]
      )
      return s.format(
          name=self.__class__.__name__, mapping=mapping, **attrs
      )
  
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 3 for calling the _BaseConvRNNCell obj's underlying member method code with example usage: obj.__repr__() and returns: s