///   * `TF_FilesystemPluginInfo` struct: similar to the above structure, but
///     collects information about all the file schemes that the plugin provides
///     support for, as well as about the plugin's memory handling routines;
///   * `TF_SetFilesystemVersionMetadata` function: must be called by plugins in
///     their `TF_InitPlugin` to record the versioning information the plugins

// - `inputs` and `outputs` cannot have reference types. Reference types are
//   not exposed through C API and are being replaced with Resources. We support
//   reference types inside function's body to support legacy code. Do not
//   use them in new code.
// - Every node in the function's body must have all of its inputs (including

to the network can execute arbitrary code with the privileges of the user
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

flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research....