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// Targeted by JavaCPP version 1.5.8: DO NOT EDIT THIS FILE

package org.bytedeco.tensorflow;

import org.bytedeco.tensorflow.Allocator;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;

import static org.bytedeco.tensorflow.global.tensorflow.*;


/** Update '*var' by subtracting 'alpha' * 'delta' from it.
 * 
 *  Arguments:
 *  * scope: A Scope object
 *  * var: Should be from a Variable().
 *  * alpha: Scaling factor. Must be a scalar.
 *  * delta: The change.
 * 
 *  Optional attributes (see {@code Attrs}):
 *  * use_locking: If {@code True}, the subtraction will be protected by a lock;
 *  otherwise the behavior is undefined, but may exhibit less contention.
 * 
 *  Returns:
 *  * {@code Output}: Same as "var". */
@Namespace("tensorflow::ops") @NoOffset @Properties(inherit = org.bytedeco.tensorflow.presets.tensorflow.class)
public class ApplyGradientDescent extends Pointer {
    static { Loader.load(); }
    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
    public ApplyGradientDescent(Pointer p) { super(p); }

  /** Optional attribute setters for ApplyGradientDescent */
  public static class Attrs extends Pointer {
      static { Loader.load(); }
      /** Default native constructor. */
      public Attrs() { super((Pointer)null); allocate(); }
      /** Native array allocator. Access with {@link Pointer#position(long)}. */
      public Attrs(long size) { super((Pointer)null); allocateArray(size); }
      /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
      public Attrs(Pointer p) { super(p); }
      private native void allocate();
      private native void allocateArray(long size);
      @Override public Attrs position(long position) {
          return (Attrs)super.position(position);
      }
      @Override public Attrs getPointer(long i) {
          return new Attrs((Pointer)this).offsetAddress(i);
      }
  
    /** If {@code True}, the subtraction will be protected by a lock;
     *  otherwise the behavior is undefined, but may exhibit less contention.
     * 
     *  Defaults to false */
    public native @ByVal Attrs UseLocking(@Cast("bool") boolean x);

    public native @Cast("bool") boolean use_locking_(); public native Attrs use_locking_(boolean setter);
  }
  public ApplyGradientDescent(@Const @ByRef Scope scope, @ByVal Input var,
                       @ByVal Input alpha, @ByVal Input delta) { super((Pointer)null); allocate(scope, var, alpha, delta); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input var,
                       @ByVal Input alpha, @ByVal Input delta);
  public ApplyGradientDescent(@Const @ByRef Scope scope, @ByVal Input var,
                       @ByVal Input alpha, @ByVal Input delta,
                       @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, var, alpha, delta, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input var,
                       @ByVal Input alpha, @ByVal Input delta,
                       @Const @ByRef Attrs attrs);
  public native @ByVal @Name("operator tensorflow::Output") Output asOutput();
  public native @ByVal @Name("operator tensorflow::Input") Input asInput();
  public native Node node();

  public static native @ByVal Attrs UseLocking(@Cast("bool") boolean x);

  public native @ByRef Operation operation(); public native ApplyGradientDescent operation(Operation setter);
  public native @ByRef Output out(); public native ApplyGradientDescent out(Output setter);
}