{"record":{"id":"f6b36462a6996df6","repo":"tensorflow/models","slug":"the-treatment-indicator-feature-specified-as-se","errorCode":null,"errorMessage":"The treatment_indicator feature (specified as '{self._treatment_indicator_feature_name}') must be part of the inputs during training and evaluation, but got input features {set(inputs.keys())} instead.","messagePattern":"The treatment_indicator feature \\(specified as '(.+?)'\\) must be part of the inputs during training and evaluation, but got input features (.+?) instead\\.","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/recommendation/uplift/models/two_tower_uplift_model.py","lineNumber":80,"sourceCode":"    self._output_head = two_tower_output_head.TwoTowerOutputHead(\n        treatment_indicator_feature_name=treatment_indicator_feature_name,\n        uplift_network=uplift_network,\n        inverse_link_fn=inverse_link_fn,\n    )\n\n  def call(\n      self,\n      inputs: types.DictOfTensors,\n      training: bool | None = None,\n      mask: tf.Tensor | None = None,\n  ) -> types.TwoTowerPredictionOutputs | types.TwoTowerTrainingOutputs:\n    return self._output_head(inputs=inputs, training=training, mask=mask)\n\n  def _assert_treatment_indicator_in_data(self, data):\n    inputs, _, _ = tf_keras.utils.unpack_x_y_sample_weight(data)\n\n    if self._treatment_indicator_feature_name not in inputs:\n      raise ValueError(\n          \"The treatment_indicator feature (specified as\"\n          f\" '{self._treatment_indicator_feature_name}') must be part of the\"\n          \" inputs during training and evaluation, but got input features\"\n          f\" {set(inputs.keys())} instead.\"\n      )\n\n  def train_step(self, data) -> types.TwoTowerTrainingOutputs:\n    self._assert_treatment_indicator_in_data(data)\n    return super().train_step(data)\n\n  def test_step(self, data) -> types.TwoTowerTrainingOutputs:\n    self._assert_treatment_indicator_in_data(data)\n    return super().test_step(data)\n\n  def predict_step(self, data) -> dict[str, tf.Tensor]:\n    outputs = super().predict_step(data)\n\n    return {","sourceCodeStart":62,"sourceCodeEnd":98,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/recommendation/uplift/models/two_tower_uplift_model.py#L62-L98","documentation":"Error \"The treatment_indicator feature (specified as '{self._treatment_indicator_feature_name}') must be part of the inputs during training and evaluation, but got input features {set(inputs.keys())} instead.\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/recommendation/uplift/models/two_tower_uplift_model.py:80 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"e006f5f0d534913e49c1f1dae87364039fa607e2","analyzedAt":"2026-08-24T14:09:15.576Z","schemaVersion":2},"datasetVersion":"2026-08-24T17:17:21.512Z"}