// enable re-resolution
                    org.eclipse.aether.artifact.Artifact art = dep.getArtifact();
                    art = art.setFile(null).setVersion(art.getBaseVersion());
                    dep = dep.setArtifact(art);
                }
                collect.addDependency(dep);
            }
        }

//		"k1":                {"arg2"},
//		"k2.k3":             {"arg3"},
//		"k4.k5.k6":          {"arg4"},
//	})
//	// preloadMap is
//	map[string]map[string][]interface{}{
//		"k0": {},
//		"k7": {
//			"k8": {},
//		},
//		"k1": {},
//		"k2": {
//			"k3": {"arg3"},
//		},
//		"k4": {
//			"k5.k6": {"arg4"},
//		},
//	}

			a = append(a, "-pthread")
		}
	}

	if cfg.Goos == "aix" {
		// mcmodel=large must always be enabled to allow large TOC.
		a = append(a, "-mcmodel=large")
	}

	// disable ASCII art in clang errors, if possible
	if b.gccSupportsFlag(compiler, "-fno-caret-diagnostics") {
		a = append(a, "-fno-caret-diagnostics")
	}
	// clang is too smart about command-line arguments

Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
random variables.
See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
Programming, Volume 2. Addison Wesley
  }];

  let arguments = (ins
    Arg<TF_I32OrI64Tensor, [{1-D integer tensor. Shape of independent samples to draw from each

*   `tf.keras`:

    *   `Model.fit` major improvements:
        *   You can now use custom training logic with `Model.fit` by overriding
            `Model.train_step`.
        *   Easily write state-of-the-art training loops without worrying about
            all of the features `Model.fit` handles for you (distribution
            strategies, callbacks, data formats, looping logic, etc)
        *   See the default