// +optional
	ParamKind *ParamKind

	// MatchConstraints specifies what resources this policy is designed to validate.
	// The AdmissionPolicy cares about a request if it matches _all_ Constraint.
	// However, in order to prevent clusters from being put into an unstable state that cannot be recovered from via the API
	// ValidatingAdmissionPolicy cannot match ValidatingAdmissionPolicy and ValidatingAdmissionPolicyBinding.

				IsOwner:         owner,
				Claims:          cred.Claims,
			}) {
				writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrAccessDenied), r.URL)
				return
			}
		}
	}

	// Parse incoming location constraint.
	_, s3Error = parseLocationConstraint(r)
	if s3Error != ErrNone {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(s3Error), r.URL)
		return
	}

  ///
  /// The output is in `buffer`, core TensorFlow owns the buffer and guarantees
  /// that at least `n` bytes are available.
  ///
  /// Returns number of bytes read or -1 in case of error. Because of this
  /// constraint and the fact that `ssize_t` is not defined in `stdint.h`/C++
  /// standard, the return type is `int64_t`.
  ///
  /// This is thread safe.
  ///
  /// Note: the `buffer` argument is NOT a null terminated string!

        // }
        //
        // In the graph above we can't cluster iteration++ with any of the
        // gradient update operations since that will break the TF resource
        // variable memory model.  Given that constraint the ideal clustering
        // would be to put all the gradient updates and all of the Computation_*
        // nodes in one cluster, and leave iteration++ and NoOp unclustered.
        //

   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *
   * This implementation uses a per-segment queue to record a memento of the additions, removals,
   * and accesses that were performed on the map. The queue is drained on writes and when it exceeds

//          must have a single output (these operations are typically
//          placeholders created for the sole purpose of representing
//          an input. We can relax this constraint if there are
//          compelling use cases).
//          - If an array is given (`num_opers` >= 0), all operations
//          in it will become part of the function. In particular, no

     * @param validation The validation to perform on the file.
     * @return The resolved file. Never returns null.
     * @throws InvalidUserDataException When the file does not meet the given validation constraint.
     */
    File file(Object path, PathValidation validation) throws InvalidUserDataException;

    /**

   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *
   * This implementation uses a per-segment queue to record a memento of the additions, removals,
   * and accesses that were performed on the map. The queue is drained on writes and when it exceeds

```
> Task :createContainer UP-TO-DATE
> Task :dockerUp UP-TO-DATE
> Task :dockerTest UP-TO-DATE
> Task :removeContainer UP-TO-DATE
> Task :dockerStop UP-TO-DATE
```

Starting with Gradle 7.5, `mustRunAfter` constraints are fully honored yielding the following order of execution:

```

					rs = tidy
				} else {
					conflict := Conflict{
						Path: mg.g.FindPath(func(m module.Version) bool {
							return m.Path == "go" && m.Version == v
						})[1:],
						Constraint: module.Version{Path: "go", Version: ld.TidyGoVersion},
					}
					msg := conflict.Summary()
					if cfg.BuildV {
						msg = conflict.String()
					}
					ld.error(errors.New(msg))
				}
			}