parameters: (self, num_traced_tensors, num_signatures, graph)
docstring: Creates a temporary cache with the given dimensions.
  
      Fills the self._temp_cache_var with num_traced_tensors tf.constant() ops
      that have shape of [num_signatures].
      Args:
        num_traced_tensors: Int, denoting total number of traced tensors.
        num_signatures: Int, denoting the number of statistics collected per
          tensors.
        graph: TensorFlow graph.
docstring_summary: Creates a temporary cache with the given dimensions.
docstring_tokens: Creates a temporary cache with the given dimensions .
function: def _create_temp_cache(self, num_traced_tensors, num_signatures, graph):
      """Creates a temporary cache with the given dimensions.
  
      Fills the self._temp_cache_var with num_traced_tensors tf.constant() ops
      that have shape of [num_signatures].
      Args:
        num_traced_tensors: Int, denoting total number of traced tensors.
        num_signatures: Int, denoting the number of statistics collected per
          tensors.
        graph: TensorFlow graph.
      """
      init_value = constant_op.constant(_COMPACT_TRACE_ENTRY_INIT_VALUE,
                                        dtype=dtypes.float32,
                                        shape=[num_signatures])
      self._temp_cache_var[graph] = [
          init_value for _ in range(num_traced_tensors)]
function_tokens: def _create_temp_cache ( self , num_traced_tensors , num_signatures , graph ) : init_value = constant_op . constant ( _COMPACT_TRACE_ENTRY_INIT_VALUE , dtype = dtypes . float32 , shape = [ num_signatures ] ) self . _temp_cache_var [ graph ] = [ init_value for _ in range ( num_traced_tensors ) ]