parameters: (self,
                            inputs,
                            weight_collections=None,
                            trainable=None)
docstring: Returns an IdWeightPair.
  
      `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
      weights.
  
      `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
      `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
      `SparseTensor` of `float` or `None` to indicate all weights should be
      taken to be 1. If specified, `weight_tensor` must have exactly the same
      shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
      output of a `VarLenFeature` which is a ragged matrix.
  
      Args:
        inputs: A `LazyBuilder` as a cache to get input tensors required to
          create `IdWeightPair`.
        weight_collections: List of graph collections to which variables (if any
          will be created) are added.
        trainable: If `True` also add variables to the graph collection
          `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.get_variable}).
docstring_summary: Returns an IdWeightPair.
docstring_tokens: Returns an IdWeightPair .
function: def _get_sparse_tensors(self,
                            inputs,
                            weight_collections=None,
                            trainable=None):
      """Returns an IdWeightPair.
  
      `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
      weights.
  
      `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
      `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
      `SparseTensor` of `float` or `None` to indicate all weights should be
      taken to be 1. If specified, `weight_tensor` must have exactly the same
      shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
      output of a `VarLenFeature` which is a ragged matrix.
  
      Args:
        inputs: A `LazyBuilder` as a cache to get input tensors required to
          create `IdWeightPair`.
        weight_collections: List of graph collections to which variables (if any
          will be created) are added.
        trainable: If `True` also add variables to the graph collection
          `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.get_variable}).
      """
      pass
function_tokens: def _get_sparse_tensors ( self , inputs , weight_collections = None , trainable = None ) : pass