docstr: proxies : P x D , proxy embeddings (one proxy per class randomly initialized, instance of nn.Parameter)
  alpha : float, random walk parameter
  lambdaC : float, regularization weight
  distance : func, distance function to use
docstr_tok: ["proxies",":","P","x","D",",","proxy","embeddings","(","one","proxy","per","class","randomly","initialized",",","instance","of","nn.Parameter",")","alpha",":","float",",","random","walk","parameter","lambdaC",":","float",",","regularization","weight","distance",":","func",",","distance","function","to","use"]
code: def __init__(
      self, proxies, alpha, lambdaC=1.0, distance=F.cosine_similarity
  ):
      """
      proxies : P x D , proxy embeddings (one proxy per class randomly initialized, instance of nn.Parameter)
      alpha : float, random walk parameter
      lambdaC : float, regularization weight
      distance : func, distance function to use
      """
      super(OriginalImplementationManifoldLoss, self).__init__()
      self.alpha = alpha
      self.lambdaC = lambdaC
      self.proxy = proxies / proxies.norm(
          p=2
      )  # Removed .cuda() to avoid errors
      self.nb_proxy = proxies.size(0)
      self.d = distance
  
file_path: pytorch-metric-learning/tests/losses/test_manifold_loss.py
imports: import unittest
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.module import Module
  from pytorch_metric_learning.losses import ManifoldLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 1 to create a new OriginalImplementationManifoldLoss object from inherited base classes: Module with example: obj = OriginalImplementationManifoldLoss(proxies, alpha, lambdaC, distance)