":tf_buffer_internal",
        ":tf_status",
        ":tf_status_helper",
        ":tf_tensor_internal",
        "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api",
        "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api_internal",
        "//tensorflow/core/platform:notification",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/strings",

        "//tensorflow:tf_private_symbols.lds",
    ],
    framework_so = [],
    linkopts = select({
        "//tensorflow:macos": [
            "-Wl,-rename_section,__TEXT,text_env,__TEXT,__text",
            "-Wl,-unexported_symbols_list,$(location //tensorflow:tf_private_symbols.lds)",
        ],
        "//tensorflow:windows": [],
        "//tensorflow:freebsd": [

        image data augmentation (which is meant to be done using preprocessing
        layers).
    *   **[`text_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory)**
        takes you from a structured directory of text files to a labeled
        dataset, in one function call.

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.