class_docstr: Cell for Sequential User Matrix
class_docstr_tok: ["Cell","for","Sequential","User","Matrix"]
docstr: Initialization operations for SUM cell.
  this function creates all the parameters for the cell.
docstr_tok: ["Initialization","operations","for","SUM","cell",".","this","function","creates","all","the","parameters","for","the","cell","."]
code: def build(self, inputs_shape):
      """Initialization operations for SUM cell.
      this function creates all the parameters for the cell.
      """
      if inputs_shape[-1] is None:
          raise ValueError(
              "Expected inputs.shape[-1] to be known, saw shape: %s"
              % str(inputs_shape)
          )
      _check_supported_dtypes(self.dtype)
      d = inputs_shape[-1]  # noqa: F841
      h = self._real_units  # noqa: F841
      s = self._slots  # noqa: F841
  
      self._basic_build(inputs_shape)
  
      self.parameter_set = [
          self._erase_W,
          self._erase_b,
          self._reset_W,
          self._reset_b,
          self._add_W,
          self._add_b,
          self.heads,
      ]
  
      self.built = True
  
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 5 for calling the SUMCell obj's underlying member method code with example usage: obj.build(inputs_shape) without return types