<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L277-L1079) |

Abstract base class for feature types.

    tfds.features.FeatureConnector(
        *, doc: DocArg = None
    )

This class provides an interface between the way the information is stored
on disk, and the way it is presented to the user.

Here is a diagram on how FeatureConnector methods fit into the data
generation/reading:

    generator => encode_example() => tf_example => decode_example() => data dict

The connector can either get raw or dictionary values as input, depending on
the connector type.

<br />

<br />

<br />

| ## Attributes ||
|---|---|
| `doc` | <br /> <br /> |
| `dtype` | Return the dtype (or dict of dtype) of this FeatureConnector. |
| `np_dtype` | <br /> <br /> |
| `numpy_dtype` | <br /> <br /> |
| `shape` | Return the shape (or dict of shape) of this FeatureConnector. |
| `tf_dtype` | <br /> <br /> |

<br />

## Methods

### `catalog_documentation`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L1002-L1031)

    catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

### `cls_from_name`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L401-L428)

    @classmethod
    cls_from_name(
        python_class_name: str
    ) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

### `decode_batch_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L780-L830)

    decode_batch_example(
        tfexample_data
    )

Decode multiple features batched in a single tf.Tensor.

This function is used to decode features wrapped in
[`tfds.features.Sequence()`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Sequence).
By default, this function apply `decode_example` on each individual
elements using [`tf.map_fn`](https://www.tensorflow.org/api_docs/python/tf/map_fn). However, for optimization, features can
overwrite this method to apply a custom batch decoding.

<br />

<br />

<br />

| Args ||
|---|---|
| `tfexample_data` | Same [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) inputs as `decode_example`, but with and additional first dimension for the sequence length. |

<br />

<br />

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| Returns ||
|---|---|
| `tensor_data` | Tensor or dictionary of tensor, output of the tf.data.Dataset object |

<br />

### `decode_batch_example_np`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L832-L834)

    decode_batch_example_np(
        batch
    )

See decode_batch_example.

### `decode_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L748-L764)

    decode_example(
        tfexample_data
    )

Decode the feature dict to TF compatible input.

> [!NOTE]
> **Note:** If eager is not enabled, this function will be executed as a tensorflow graph (in [`tf.data.Dataset.map(features.decode_example)`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map)).

<br />

<br />

<br />

| Args ||
|---|---|
| `tfexample_data` | Data or dictionary of data, as read by the tf-example reader. It correspond to the [`tf.Tensor()`](https://www.tensorflow.org/api_docs/python/tf/Tensor) (or dict of [`tf.Tensor()`](https://www.tensorflow.org/api_docs/python/tf/Tensor)) extracted from the [`tf.train.Example`](https://www.tensorflow.org/api_docs/python/tf/train/Example), matching the info defined in `get_serialized_info()`. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `tensor_data` | Tensor or dictionary of tensor, output of the tf.data.Dataset object |

<br />

### `decode_example_np`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L766-L778)

    decode_example_np(
        example_data: type_utils.NpArrayOrScalar
    ) -> (type_utils.NpArrayOrScalar | None)

Decode the example data into NumPy-compatible input.

<br />

<br />

<br />

| Args ||
|---|---|
| `example_data` | Value to convert to NumPy. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `np_data` | Data as NumPy-compatible type: either a Python primitive (bytes, int, etc) or a NumPy array. |

<br />

### `decode_ragged_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L836-L853)

    decode_ragged_example(
        tfexample_data
    )

Decode nested features from a tf.RaggedTensor.

This function is used to decode features wrapped in nested
[`tfds.features.Sequence()`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Sequence).
By default, this function apply `decode_batch_example` on the flat values
of the ragged tensor. For optimization, features can
overwrite this method to apply a custom batch decoding.

<br />

<br />

<br />

| Args ||
|---|---|
| `tfexample_data` | [`tf.RaggedTensor`](https://www.tensorflow.org/api_docs/python/tf/RaggedTensor) inputs containing the nested encoded examples. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `tensor_data` | The decoded [`tf.RaggedTensor`](https://www.tensorflow.org/api_docs/python/tf/RaggedTensor) or dictionary of tensor, output of the tf.data.Dataset object |

<br />

### `encode_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L701-L746)

    @abc.abstractmethod
    encode_example(
        example_data
    )

Encode the feature dict into tf-example compatible input.

The input example_data can be anything that the user passed at data
generation. For example:

#### For features:

    features={
        'image': tfds.features.Image(),
        'custom_feature': tfds.features.CustomFeature(),
    }

At data generation (in `_generate_examples`), if the user yields:

    yield {
        'image': 'path/to/img.png',
        'custom_feature': [123, 'str', lambda x: x+1]
    }

<br />

<br />

| Then ||
|---|---|
| <br /> - [`tfds.features.Image.encode_example`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Image#encode_example) will get `'path/to/img.png'` as input - `tfds.features.CustomFeature.encode_example` will get \`\[123, 'str', lambda x: x+1\] as input ||

<br />

<br />

<br />

<br />

| Args ||
|---|---|
| `example_data` | Value or dictionary of values to convert into tf-example compatible data. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `tfexample_data` | Data or dictionary of data to write as tf-example. Data can be a list or numpy array. Note that numpy arrays are flattened so it's the feature connector responsibility to reshape them in `decode_example()`. Note that tf.train.Example only supports int64, float32 and string so the data returned here should be integer, float or string. User type can be restored in `decode_example()`. |

<br />

### `from_config`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L646-L665)

    @classmethod
    from_config(
        root_dir: str
    ) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

#### Usage:

    features = FeatureConnector.from_config('path/to/dir')

<br />

<br />

<br />

| Args ||
|---|---|
| `root_dir` | Directory containing the features.json file. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The reconstructed feature instance. ||

<br />

### `from_json`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L434-L480)

    @classmethod
    from_json(
        value: Json
    ) -> FeatureConnector

FeatureConnector factory.

This function should be called from the [`tfds.features.FeatureConnector`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeatureConnector)
base class. Subclass should implement the `from_json_content`.

#### Example:

    feature = tfds.features.FeatureConnector.from_json(
        {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
    )
    assert isinstance(feature, tfds.features.Image)

<br />

<br />

<br />

| Args ||
|---|---|
| `value` | `dict(type=, content=)` containing the feature to restore. Match dict returned by `to_json`. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The reconstructed FeatureConnector. ||

<br />

### `from_json_content`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L547-L574)

    @classmethod
    from_json_content(
        value: Union[Json, message.Message], doc: Optional[DocArg] = None
    ) -> T

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when
importing the feature connector from the config.

This function should not be called directly. [`FeatureConnector.from_json`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeatureConnector#from_json)
should be called instead.

See existing FeatureConnectors for implementation examples.

<br />

<br />

<br />

| Args ||
|---|---|
| `value` | FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by `to_json_content`. |
| `doc` | Documentation of this feature (e.g. description). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The reconstructed FeatureConnector. ||

<br />

### `from_proto`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L618-L632)

    @classmethod
    from_proto(
        feature_proto: feature_pb2.Feature
    ) -> T

Instantiates a feature from its proto representation.

### `get_serialized_info`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L667-L699)

    get_serialized_info() -> Union[TensorInfo, Mapping[str, TensorInfo]]

Return the shape/dtype of features after encoding (for the adapter).

The `FileAdapter` then use those information to write data on disk.

This function indicates how this feature is encoded on file internally.
The DatasetBuilder are written on disk as tf.train.Example proto.

#### Ex:

    return {
        'image': tfds.features.TensorInfo(shape=(None,), dtype=np.uint8),
        'height': tfds.features.TensorInfo(shape=(), dtype=np.int32),
        'width': tfds.features.TensorInfo(shape=(), dtype=np.int32),
    }

FeatureConnector which are not containers should return the feature proto
directly:

    return tfds.features.TensorInfo(shape=(64, 64), np.uint8)

If not defined, the retuned values are automatically deduced from the
`get_tensor_info` function.

<br />

<br />

<br />

| Returns ||
|---|---|
| `features` | Either a dict of feature proto object, or a feature proto object |

<br />

### `get_tensor_info`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L331-L359)

    @abc.abstractmethod
    get_tensor_info() -> TreeDict[TensorInfo]

Return the tf.Tensor dtype/shape of the feature.

This returns the tensor dtype/shape, as returned by .as_dataset by the
[`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) object.

#### Ex:

    return {
        'image': tfds.features.TensorInfo(shape=(None,), dtype=np.uint8),
        'height': tfds.features.TensorInfo(shape=(), dtype=np.int32),
        'width': tfds.features.TensorInfo(shape=(), dtype=np.int32),
    }

FeatureConnector which are not containers should return the feature proto
directly:

    return tfds.features.TensorInfo(shape=(256, 256), dtype=np.uint8)

<br />

<br />

<br />

| Returns ||
|---|---|
| `tensor_info` | Either a dict of [`tfds.features.TensorInfo`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/TensorInfo) object, or a [`tfds.features.TensorInfo`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/TensorInfo) |

<br />

### `get_tensor_spec`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L361-L370)

    get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec
of the dataset. For example, currently this method does not support
RaggedTensorSpec.

### `load_metadata`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L1064-L1079)

    load_metadata(
        data_dir: epath.PathLike, feature_name: Optional[str]
    )

Restore the feature metadata from disk.

If a dataset is re-loaded and generated files exists on disk, this function
will restore the feature metadata from the saved file.

<br />

<br />

<br />

| Args ||
|---|---|
| `data_dir` | path to the dataset folder where the info is saved (ex: `~/datasets/cifar10/1.2.0/`) |
| `feature_name` | the name of the feature (from the FeaturesDict key) |

<br />

### `repr_html`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L855-L857)

    repr_html(
        ex: np.ndarray
    ) -> str

Returns the HTML str representation of the object.

### `repr_html_batch`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L859-L874)

    repr_html_batch(
        ex: np.ndarray
    ) -> str

Returns the HTML str representation of the object (Sequence).

### `repr_html_ragged`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L876-L878)

    repr_html_ragged(
        ex: np.ndarray
    ) -> str

Returns the HTML str representation of the object (Nested sequence).

### `save_config`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L634-L644)

    save_config(
        root_dir: str
    ) -> None

Exports the `FeatureConnector` to a file.

<br />

<br />

<br />

| Args ||
|---|---|
| `root_dir` | `path/to/dir` containing the `features.json` |

<br />

### `save_metadata`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L1033-L1062)

    save_metadata(
        data_dir: epath.PathLike, feature_name: Optional[str]
    ) -> None

Save the feature metadata on disk.

This function is called after the data has been generated (by
`_download_and_prepare`) to save the feature connector info with the
generated dataset.

Some dataset/features dynamically compute info during
`_download_and_prepare`. For instance:

- Labels are loaded from the downloaded data
- Vocabulary is created from the downloaded data
- ImageLabelFolder compute the image dtypes/shape from the manual_dir

After the info have been added to the feature, this function allow to
save those additional info to be restored the next time the data is loaded.

By default, this function do not save anything, but sub-classes can
overwrite the function.

<br />

<br />

<br />

| Args ||
|---|---|
| `data_dir` | path to the dataset folder to which save the info (ex: `~/datasets/cifar10/1.2.0/`) |
| `feature_name` | the name of the feature (from the FeaturesDict key) |

<br />

### `to_json`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L482-L545)

    to_json() -> Json

Exports the FeatureConnector to Json.

Each feature is serialized as a `dict(type=..., content=...)`.

- `type`: The cannonical name of the feature (`module.FeatureName`).
- `content`: is specific to each feature connector and defined in `to_json_content`. Can contain nested sub-features (like for [`tfds.features.FeaturesDict`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeaturesDict) and [`tfds.features.Sequence`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Sequence)).

#### For example:

    tfds.features.FeaturesDict({
        'input': tfds.features.Image(),
        'target': tfds.features.ClassLabel(num_classes=10),
    })

#### Is serialized as:

    {
        "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
        "content": {
            "input": {
                "type": "tensorflow_datasets.core.features.image_feature.Image",
                "content": {
                    "shape": [null, null, 3],
                    "dtype": "uint8",
                    "encoding_format": "png"
                }
            },
            "target": {
                "type":
                "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
                "content": {
                  "num_classes": 10
                }
            }
        }
    }

<br />

<br />

<br />

| Returns ||
|---|---|
| A `dict(type=, content=)`. Will be forwarded to `from_json` when reconstructing the feature. ||

<br />

### `to_json_content`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L576-L587)

    to_json_content() -> Union[Json, message.Message]

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing
the feature connector from the config. See existing FeatureConnector for
example of implementation.

<br />

<br />

<br />

| Returns ||
|---|---|
| The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in `from_json_content` when reconstructing the feature. ||

<br />

### `to_proto`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L589-L616)

    to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this
function needs to be overriden. If there's no specific proto schema,
then the feature will be represented using JSON.

<br />

<br />

<br />

| Returns ||
|---|---|
| The feature proto describing this feature. ||

<br />

<br />

<br />

<br />

| ## Class Variables ||
|---|---|
| ALIASES | `[]` |

<br />