message: Fix inconsistent tensor ranks in _linear_predictions
  * Fix inconsistent tensor ranks in _linear_predictions
  When mixing sparse feature columns and dense tensors of features _linear_predictions behave anormaly without this fix (and is unusable).
  * Fix formatting
diff: @@ -238,10 +238,10 @@ class SdcaModel(object):
  with name_scope('sdca/prediction'):
  sparse_variables = self._convert_n_to_tensor(self._variables[
  'sparse_features_weights'])
  - result = 0.0
  + result_sparse = 0.0
  for sfc, sv in zip(examples['sparse_features'], sparse_variables):
  # TODO(sibyl-Aix6ihai): following does not take care of missing features.
  - result += math_ops.segment_sum(
  + result_sparse += math_ops.segment_sum(
  math_ops.multiply(
  array_ops.gather(sv, sfc.feature_indices), sfc.feature_values),
  sfc.example_indices)
  @@ -249,12 +249,13 @@ class SdcaModel(object):
  dense_variables = self._convert_n_to_tensor(self._variables[
  'dense_features_weights'])
  + result_dense = 0.0
  for i in range(len(dense_variables)):
  - result += math_ops.matmul(dense_features[i],
  - array_ops.expand_dims(dense_variables[i], -1))
  + result_dense += math_ops.matmul(
  + dense_features[i], array_ops.expand_dims(dense_variables[i], -1))
  # Reshaping to allow shape inference at graph construction time.
  - return array_ops.reshape(result, [-1])
  + return array_ops.reshape(result_dense, [-1]) + result_sparse
  def predictions(self, examples):
  """Add operations to compute predictions by the model.
  