{"record":{"id":"b91e247513f8dc61","repo":"tensorflow/models","slug":"at-least-one-of-encoder-input-tokens-and-encoder-d","errorCode":null,"errorMessage":"At least one of encoder_input_tokens and encoder_dense_inputs must be provided.","messagePattern":"At least one of encoder_input_tokens and encoder_dense_inputs must be provided\\.","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/nlp/modeling/models/t5.py","lineNumber":1433,"sourceCode":"          compute_dtype=self.compute_dtype)\n\n  def encode(self,\n             encoder_input_tokens=None,\n             encoder_segment_ids=None,\n             encoder_dense_inputs=None,\n             encoder_dense_segment_ids=None,\n             training=False):\n    eligible_position_array = []\n    if encoder_input_tokens is not None:\n      eligible_position_array.append(\n          tf.cast(tf.not_equal(encoder_input_tokens, 0), self.compute_dtype))\n    if encoder_dense_inputs is not None:\n      eligible_dense_positions = tf.cast(\n          tf.reduce_any(tf.not_equal(encoder_dense_inputs, 0), axis=-1),\n          self.compute_dtype)\n      eligible_position_array.append(eligible_dense_positions)\n    if not eligible_position_array:\n      raise ValueError(\"At least one of encoder_input_tokens and\"\n                       \" encoder_dense_inputs must be provided.\")\n\n    eligible_positions = tf.concat(eligible_position_array, axis=1)\n    encoder_mask = make_attention_mask(\n        eligible_positions, eligible_positions, dtype=tf.bool)\n\n    encoder_segment_id_array = []\n    if encoder_segment_ids is not None:\n      encoder_segment_id_array.append(encoder_segment_ids)\n    if encoder_dense_segment_ids is not None:\n      encoder_segment_id_array.append(encoder_dense_segment_ids)\n    if encoder_segment_id_array:\n      encoder_segment_ids = tf.concat(encoder_segment_id_array, axis=1)\n      segment_mask = make_attention_mask(\n          encoder_segment_ids, encoder_segment_ids, tf.equal, dtype=tf.bool)\n      encoder_mask = tf.math.logical_and(encoder_mask, segment_mask)\n    encoder_mask = (1.0 - tf.cast(encoder_mask, self.compute_dtype)) * -1e9\n    return self.encoder(","sourceCodeStart":1415,"sourceCodeEnd":1451,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/nlp/modeling/models/t5.py#L1415-L1451","documentation":"Error \"At least one of encoder_input_tokens and encoder_dense_inputs must be provided.\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/nlp/modeling/models/t5.py:1433 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"}