package org.example;

import org.gradle.api.DefaultTask;
import org.gradle.api.tasks.TaskAction;

public class GreetingTask extends DefaultTask {
    @TaskAction
    public void greet() {
        System.out.println("Hello from Gradle 7+");
    }

- Import from GraphDef, in .pb or .pbtxt  format, into MLIR.
- Raise to Control-flow-graph. Converts TF Control Flow dialect to TF dialect.
- The Canonicalization pass iteratively applies canonicalization
transformations in a greedy way until no further changes occur.
Canonicalization includes constant folding.
- The Legalize pass converts TensorFlow operations to TensorFlow Lite
ones. The operations that cannot be mapped to TensorFlow Lite dialect

import org.gradle.api.tasks.TaskAction
import org.gradle.api.tasks.Input

class GreetingTask extends DefaultTask {

    @Input
    String greeting = 'hello from GreetingTask'

    @TaskAction
    def greet() {
        println greeting
    }

      <id>carlos</id>
      <email>******@****.***</email>
      <roles>
        <role>Developer</role>
      </roles>
    </developer>
    <developer>
      <name>Brett Porter</name>
      <id>brett</id>
      <email>brett@codehaus.org</email>
      <roles>
        <role>Developer</role>
      </roles>
    </developer>
    <developer>
      <name>John Casey</name>
      <id>jdcasey</id>

package org.example;

import org.gradle.api.DefaultTask;
import org.gradle.api.tasks.TaskAction;

public class GreetingTask extends DefaultTask {
    @TaskAction
    public void greet() {
        System.out.println("Hello from Gradle <7");
    }

    private final PathMatcher next;

    public GreedyPathMatcher(PathMatcher next) {
        this.next = next;
    }

    @Override
    public String toString() {
        return "{greedy next: " + next + "}";
    }

    @Override
    public int getMaxSegments() {
        return Integer.MAX_VALUE;
    }

    @Override
    public int getMinSegments() {
        return next.getMinSegments();

	// Profile calls Modularize which implements the Louvain modularization algorithm.
	// Since this is a randomized algorithm we use a defined random source to ensure
	// consistency between test runs. In practice, results will not differ greatly
	// between runs with different PRNG seeds.
	src := rand.NewSource(1)

	// Create dumbell graph:
	//
	//  0       4
	//  |\     /|
	//  | 2 - 3 |
	//  |/     \|
	//  1       5
	//

import org.gradle.api.tasks.TaskAction
import org.gradle.api.tasks.Input

class GreetingTask extends DefaultTask {

    @Input
    String greeting = 'hello from GreetingTask'

    @TaskAction
    def greet() {
        println greeting
    }

  //
  // For example some tensor handles may represent distributed values, in which
  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.

      <id>carlos</id>
      <email>******@****.***</email>
      <roles>
        <role>Developer</role>
      </roles>
    </developer>
    <developer>
      <name>Brett Porter</name>
      <id>brett</id>
      <email>brett@codehaus.org</email>
      <roles>
        <role>Developer</role>
      </roles>
    </developer>
    <developer>
      <name>John Casey</name>
      <id>jdcasey</id>