```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.

 Plugins should avoid these classes and be updated to use only Maven3 dependencies (and require Maven3): see
 {{{https://cwiki.apache.org/confluence/display/MAVEN/Plugin+migration+to+Maven3+dependencies} Plugin migration to Maven3 dependencies}}

     *
     * @return an {@link Optional} containing the directory path, or empty if not specified
     */
    @Nonnull
    Optional<String> directory();

    /**
     * Should use inference when upgrading (remove redundant information).
     *
     * @return an {@link Optional} containing the boolean value {@code true} if specified, or empty
     */
    @Nonnull
    Optional<Boolean> infer();

    /**

 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.PluginUpgradeStrategy} - Upgrades plugin versions for Maven 4 compatibility</li>
 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.InferenceStrategy} - Applies Maven 4.1.0+ inference optimizations</li>
 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.CompatibilityFixStrategy} - Fixes Maven 4 compatibility issues</li>
 * </ul>
 *
 * <h3>Utility Classes</h3>
 * <ul>

func (v *Value) InferBytesType() (err error) {
	b, ok := v.ToBytes()
	if !ok {
		return fmt.Errorf("InferByteType: Input is not bytes, but %v", v.GetTypeString())
	}

	// Check for numeric inference
	if x, ok := v.bytesToInt(); ok {
		v.setInt(x)
		return nil
	}
	if x, ok := v.bytesToFloat(); ok {
		v.setFloat(x)
		return nil
	}
	if x, ok := v.bytesToBool(); ok {
		v.setBool(x)

“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. In this paper, we describe the TensorFlow dataflow model and...

// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.

    {{{https://cwiki.apache.org/confluence/display/MAVEN/POM+Element+for+Source+File+Encoding}source files encoding}}
    (defaults to <<<UTF-8>>> since Maven 4.0.0, no default value was provided in Maven 3.x, meaning that the platform
    encoding was used by plugins)

    * <<<$\{project.reporting.outputEncoding\}>>> for

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack