class_docstr: Implementation Focal cosine loss from the "Data-Efficient Deep Learning Method for Image Classification
  Using Data Augmentation, Focal Cosine Loss, and Ensemble" (https://arxiv.org/abs/2007.07805).
  
  Credit: https://www.kaggle.com/c/cassava-leaf-disease-classification/discussion/203271
code: def forward(self, input: Tensor, target: Tensor) -> Tensor:
      cosine_loss = F.cosine_embedding_loss(
          input,
          torch.nn.functional.one_hot(
              target, num_classes=input.size(-1)
          ),
          torch.tensor([1], device=target.device),
          reduction=self.reduction,
      )
  
      cent_loss = F.cross_entropy(
          F.normalize(input), target, reduction="none"
      )
      pt = torch.exp(-cent_loss)
      focal_loss = self.alpha * (1 - pt) ** self.gamma * cent_loss
  
      if self.reduction == "mean":
          focal_loss = torch.mean(focal_loss)
  
      return cosine_loss + self.xent * focal_loss
  
imports: from typing import Optional
  from torch import nn, Tensor
  import torch.nn.functional
  import torch
label_desc: Use image node_id 2 for calling the FocalCosineLoss obj's underlying member method code with example usage: obj.forward(input, target) and returns: unknown