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/classifier.py:167

    self.inputs = inputs
    self._cls_head = cls_head
    self._name = name
    self.classifier = classifier

  def call(self, inputs):  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
    """Return perceiver classifier 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`.

    Returns:
      `tf.Tensor` classification output.
    """
    if not isinstance(inputs, dict):
      raise ValueError(f'Unexpected inputs type to {self.__class__}.')

    word_ids = inputs['input_word_ids']
    input_type_ids = inputs.get('input_type_ids')
    input_mask = inputs.get('input_mask')

    encoder_inputs = {
        'input_word_ids': word_ids,
        'input_mask': input_mask,
        'input_type_ids': input_type_ids,
    }
    encoder_outputs = self._network(encoder_inputs)

    if 'sequence_output' not in encoder_outputs:
      if 'latent_output' in encoder_outputs:
        z = encoder_outputs['latent_output']
        decoder_inputs = {'latent_output': z, 'input_mask': input_mask}
        decoder_output = self._decoder(decoder_inputs)  # pyrefly: ignore[not-callable]

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/perceiver/modeling/models/classifier.py:167 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/895664fd17009549. Report an issue: GitHub.