class_docstr: Cell for Sequential User Matrix
class_docstr_tok: ["Cell","for","Sequential","User","Matrix"]
code: def __init__(
      self,
      num_units,
      slots,
      attention_size,
      input_size,
      activation=None,
      reuse=None,
      kernel_initializer=None,
      bias_initializer=None,
      name=None,
      dtype=None,
      **kwargs,
  ):
      super(SUMCell, self).__init__(
          _reuse=reuse, name=name, dtype=dtype, **kwargs
      )
      _check_supported_dtypes(self.dtype)
  
      if context.executing_eagerly() and context.num_gpus() > 0:
          logging.warn(
              "%s: Note that this cell is not optimized for performance. "
              "Please use keras.layers.cudnn_recurrent.CuDNNGRU for better "
              "performance on GPU.",
              self,
          )
  
      self._input_size = input_size
      self._slots = (
          slots - 1
      )  # the last channel is reserved for the highway slot
      self._num_units = num_units
      self._real_units = (self._num_units - input_size) // slots
      if activation:
          self._activation = activations.get(activation)
      else:
          self._activation = math_ops.tanh
      self._kernel_initializer = initializers.get(kernel_initializer)
      self._bias_initializer = initializers.get(bias_initializer)
  
imports: import numpy
  import tensorflow
  from keras.layers.legacy_rnn.rnn_cell_impl import LayerRNNCell
  from tensorflow.python.eager import context
  from tensorflow.python.keras import activations
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.ops import init_ops
  from tensorflow.python.framework import dtypes
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 to create a new SUMCell object from inherited base classes: LayerRNNCell with example: obj = SUMCell(num_units, slots, attention_size, input_size, activation, reuse, kernel_initializer, bias_initializer, name, dtype)