class_docstr: Feature Pyramid Loss This class implmenents the feature pyramid loss function proposed in STFPM paper.
  
  Example:
      >>> from anomalib.models.components.feature_extractors import FeatureExtractor
      >>> from anomalib.models.stfpm.loss import STFPMLoss
      >>> from torchvision.models import resnet18
  
      >>> layers = ['layer1', 'layer2', 'layer3']
      >>> teacher_model = FeatureExtractor(model=resnet18(pretrained=True), layers=layers)
      >>> student_model = FeatureExtractor(model=resnet18(pretrained=False), layers=layers)
      >>> loss = Loss()
  
      >>> inp = torch.rand((4, 3, 256, 256))
      >>> teacher_features = teacher_model(inp)
      >>> student_features = student_model(inp)
      >>> loss(student_features, teacher_features)
          tensor(51.2015, grad_fn=<SumBackward0>)
code: def __init__(self) -> None:
      super().__init__()
      self.mse_loss = nn.MSELoss(reduction="sum")
  
file_path: anomalib/src/anomalib/models/stfpm/loss.py
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new STFPMLoss object from inherited base classes: nn with example: obj = STFPMLoss()