tensorflow/models · error · ValueError
Unexpected inputs type to {self.__class__}.
Error message
Unexpected inputs type to {self.__class__}. What it means
Error "Unexpected inputs type to {self.__class__}." thrown in tensorflow/models.
Source
Thrown at official/projects/perceiver/modeling/models/pretrainer.py:132
encoder_inputs = copy.copy(self.encoder.inputs)
inputs = dict(encoder_inputs)
if self._decoder is not None:
inputs.update(copy.copy(self._decoder.inputs))
self.masked_lm = customized_masked_lm or layers.MaskedLM(
embedding_table=self.encoder.get_embedding_table(),
activation=mlm_activation,
initializer=mlm_initializer,
name='cls/predictions')
masked_lm_positions = tf_keras.layers.Input(
shape=(None,), name='masked_lm_positions', dtype=tf.int32)
if isinstance(inputs, dict):
inputs['masked_lm_positions'] = masked_lm_positions
else:
raise ValueError(f'Unexpected inputs type to {self.__class__}.')
self.inputs = inputs
def call(self, inputs): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
"""Return perceiver pretrainer model output tensors in a dict.
Accepts inputs as dictionary of tensors.
Args:
inputs:
A `Dict[str, tf_keras.Input]` with `input_word_ids`, `input_mask`, and
`input_type_ids`. The shapes are all `(None)` with dtype `tf.int32`.
If `masked_lm_positions` is included, it will run masked language
modeling layer to return sequence of logits.
Returns:
`Dict[str, tf.Tensor]` with `sequence_output` and optionally
`mlm_logits`.
"""
if not isinstance(inputs, dict):View on GitHub (pinned to e006f5f0d5)
When it happens
Trigger: Thrown at official/projects/perceiver/modeling/models/pretrainer.py:132 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/97be765081920763.
Report an issue: GitHub.