|---| | [ View source on GitHub](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/export/export.py#L165-L220) | A return type for a serving_input_receiver_fn. (deprecated) ```python tf.estimator.export.TensorServingInputReceiver( features, receiver_tensors, receiver_tensors_alternatives=None ) ``` > [!WARNING] > **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.keras instead. This is for use with models that expect a single `Tensor` or `SparseTensor` as an input feature, as opposed to a dict of features. The normal `ServingInputReceiver` always returns a feature dict, even if it contains only one entry, and so can be used only with models that accept such a dict. For models that accept only a single raw feature, the `serving_input_receiver_fn` provided to [`Estimator.export_saved_model()`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/compat/v1/estimator/Estimator.md.txt#export_saved_model) should return this `TensorServingInputReceiver` instead. See: https://github.com/tensorflow/tensorflow/issues/11674 Note that the receiver_tensors and receiver_tensor_alternatives arguments will be automatically converted to the dict representation in either case, because the SavedModel format requires each input `Tensor` to have a name (provided by the dict key).


| ## Attributes || |---|---| | `features` | A single `Tensor` or `SparseTensor`, representing the feature to be passed to the model. | | `receiver_tensors` | A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or `SparseTensor`, specifying input nodes where this receiver expects to be fed by default. Typically, this is a single placeholder expecting serialized `tf.Example` protos. | | `receiver_tensors_alternatives` | a dict of string to additional groups of receiver tensors, each of which may be a `Tensor`, `SparseTensor`, or dict of string to `Tensor` or`SparseTensor`. These named receiver tensor alternatives generate additional serving signatures, which may be used to feed inputs at different points within the input receiver subgraph. A typical usage is to allow feeding raw feature `Tensor`s *downstream* of the tf.parse_example() op. Defaults to None. |