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// Targeted by JavaCPP version 1.5.11: DO NOT EDIT THIS FILE

package org.bytedeco.pytorch;

import org.bytedeco.pytorch.Allocator;
import org.bytedeco.pytorch.Function;
import org.bytedeco.pytorch.Module;
import org.bytedeco.javacpp.annotation.Cast;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.javacpp.chrono.*;
import static org.bytedeco.javacpp.global.chrono.*;

import static org.bytedeco.pytorch.global.torch.*;


// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CosineEmbeddingLoss
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/** Creates a criterion that measures the loss given input tensors
 *  {@code input1}, {@code input2}, and a {@code Tensor} label {@code target} with values 1 or
 *  -1. This is used for measuring whether two inputs are similar or
 *  dissimilar, using the cosine distance, and is typically used for learning
 *  nonlinear embeddings or semi-supervised learning.
 *  See https://pytorch.org/docs/main/nn.html#torch.nn.CosineEmbeddingLoss to
 *  learn about the exact behavior of this module.
 * 
 *  See the documentation for {@code torch::nn::CosineEmbeddingLossOptions} class to
 *  learn what constructor arguments are supported for this module.
 * 
 *  Example:
 *  {@code
 *  CosineEmbeddingLoss model(CosineEmbeddingLossOptions().margin(0.5));
 *  } */
@Namespace("torch::nn") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class)
public class CosineEmbeddingLossImpl extends CosineEmbeddingLossImplCloneable {
    static { Loader.load(); }
    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
    public CosineEmbeddingLossImpl(Pointer p) { super(p); }

  public CosineEmbeddingLossImpl(@ByVal(nullValue = "torch::nn::CosineEmbeddingLossOptions{}") CosineEmbeddingLossOptions options_) { super((Pointer)null); allocate(options_); }
  @SharedPtr @Name("std::make_shared") private native void allocate(@ByVal(nullValue = "torch::nn::CosineEmbeddingLossOptions{}") CosineEmbeddingLossOptions options_);
  public CosineEmbeddingLossImpl() { super((Pointer)null); allocate(); }
  @SharedPtr @Name("std::make_shared") private native void allocate();

  public native void reset();

  /** Pretty prints the {@code CosineEmbeddingLoss} module into the given {@code stream}. */
  public native void pretty_print(@Cast("std::ostream*") @ByRef Pointer stream);

  public native @ByVal Tensor forward(
        @Const @ByRef Tensor input1,
        @Const @ByRef Tensor input2,
        @Const @ByRef Tensor target);

  /** The options with which this {@code Module} was constructed. */
  public native @ByRef CosineEmbeddingLossOptions options(); public native CosineEmbeddingLossImpl options(CosineEmbeddingLossOptions setter);
}