{"record":{"id":"af45c70f23ab06d7","repo":"tensorflow/models","slug":"unknown-output-value-s-output-can-be-eithe-af45c7","errorCode":null,"errorMessage":"Unknown `output` value \"%s\". `output` can be either \"logits\" or \"predictions\"","messagePattern":"Unknown `output` value \"(.+?)\"\\. `output` can be either \"logits\" or \"predictions\"","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/projects/teams/teams_pretrainer.py","lineNumber":74,"sourceCode":"              num_attention_heads=self.hidden_cfg['num_attention_heads'],\n              intermediate_size=self.hidden_cfg['intermediate_size'],\n              intermediate_activation=self.activation,\n              dropout_rate=self.hidden_cfg['dropout_rate'],\n              attention_dropout_rate=self.hidden_cfg['attention_dropout_rate'],\n              kernel_initializer=tf_utils.clone_initializer(self.initializer),\n              name='transformer/layer_%d_rtd' % i))\n    self.dense = tf_keras.layers.Dense(\n        self.hidden_size,\n        activation=self.activation,\n        kernel_initializer=tf_utils.clone_initializer(self.initializer),\n        name='transform/rtd_dense')\n    self.rtd_head = tf_keras.layers.Dense(\n        units=1,\n        kernel_initializer=tf_utils.clone_initializer(self.initializer),\n        name='transform/rtd_head')\n\n    if output not in ('predictions', 'logits'):\n      raise ValueError(\n          ('Unknown `output` value \"%s\". `output` can be either \"logits\" or '\n           '\"predictions\"') % output)\n    self._output_type = output\n\n  def call(self, sequence_data, input_mask):\n    \"\"\"Compute inner-products of hidden vectors with sampled element embeddings.\n\n    Args:\n      sequence_data: A [batch_size, seq_length, num_hidden] tensor.\n      input_mask: A [batch_size, seq_length] binary mask to separate the input\n        from the padding.\n\n    Returns:\n      A [batch_size, seq_length] tensor.\n    \"\"\"\n    attention_mask = layers.SelfAttentionMask()([sequence_data, input_mask])\n    data = sequence_data\n    for hidden_layer in self.hidden_layers:","sourceCodeStart":56,"sourceCodeEnd":92,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/projects/teams/teams_pretrainer.py#L56-L92","documentation":"Error \"Unknown `output` value \"%s\". `output` can be either \"logits\" or \"predictions\"\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/projects/teams/teams_pretrainer.py:74 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"}