parameters: (columns_to_tensors,
                                     embedding_lookup_arguments,
                                     num_outputs,
                                     trainable,
                                     weight_collections)
docstring: Creates an embedding lookup for all columns sharing a single weight.
docstring_summary: Creates an embedding lookup for all columns sharing a single weight.
docstring_tokens: Creates an embedding lookup for all columns sharing a single weight .
function: def _create_joint_embedding_lookup(columns_to_tensors,
                                     embedding_lookup_arguments,
                                     num_outputs,
                                     trainable,
                                     weight_collections):
    """Creates an embedding lookup for all columns sharing a single weight."""
    for arg in embedding_lookup_arguments:
      assert arg.weight_tensor is None, (
          'Joint sums for weighted sparse columns are not supported. '
          'Please use weighted_sum_from_feature_columns instead.')
      assert arg.combiner == 'sum', (
          'Combiners other than sum are not supported for joint sums. '
          'Please use weighted_sum_from_feature_columns instead.')
    assert len(embedding_lookup_arguments) >= 1, (
        'At least one column must be in the model.')
    prev_size = 0
    sparse_tensors = []
    for a in embedding_lookup_arguments:
      t = a.input_tensor
      values = t.values + prev_size
      prev_size += a.vocab_size
      sparse_tensors.append(
          sparse_tensor_py.SparseTensor(t.indices,
                                        values,
                                        t.dense_shape))
    sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
    with variable_scope.variable_scope(
        None, default_name='linear_weights', values=columns_to_tensors.values()):
      variable = contrib_variables.model_variable(
          name='weights',
          shape=[prev_size, num_outputs],
          dtype=dtypes.float32,
          initializer=init_ops.zeros_initializer(),
          trainable=trainable,
          collections=weight_collections)
      if fc._is_variable(variable):  # pylint: disable=protected-access
        variable = [variable]
      else:
        variable = variable._get_variable_list()  # pylint: disable=protected-access
      predictions = embedding_ops.safe_embedding_lookup_sparse(
          variable,
          sparse_tensor,
          sparse_weights=None,
          combiner='sum',
          name='_weights')
      return variable, predictions
function_tokens: def _create_joint_embedding_lookup ( columns_to_tensors , embedding_lookup_arguments , num_outputs , trainable , weight_collections ) : for arg in embedding_lookup_arguments : assert arg . weight_tensor is None , ( 'Joint sums for weighted sparse columns are not supported. ' 'Please use weighted_sum_from_feature_columns instead.' ) assert arg . combiner == 'sum' , ( 'Combiners other than sum are not supported for joint sums. ' 'Please use weighted_sum_from_feature_columns instead.' ) assert len ( embedding_lookup_arguments ) >= 1 , ( 'At least one column must be in the model.' ) prev_size = 0 sparse_tensors = [ ] for a in embedding_lookup_arguments : t = a . input_tensor values = t . values + prev_size prev_size += a . vocab_size sparse_tensors . append ( sparse_tensor_py . SparseTensor ( t . indices , values , t . dense_shape ) ) sparse_tensor = sparse_ops . sparse_concat ( 1 , sparse_tensors ) with variable_scope . variable_scope ( None , default_name = 'linear_weights' , values = columns_to_tensors . values ( ) ) : variable = contrib_variables . model_variable ( name = 'weights' , shape = [ prev_size , num_outputs ] , dtype = dtypes . float32 , initializer = init_ops . zeros_initializer ( ) , trainable = trainable , collections = weight_collections ) if fc . _is_variable ( variable ) : # pylint: disable=protected-access variable = [ variable ] else : variable = variable . _get_variable_list ( ) # pylint: disable=protected-access predictions = embedding_ops . safe_embedding_lookup_sparse ( variable , sparse_tensor , sparse_weights = None , combiner = 'sum' , name = '_weights' ) return variable , predictions