class_docstr: Implementation of the Softmax loss as defined in https://arxiv.org/abs/2003.11982
      Args:
          - embedding_dim (float): speaker embedding dim
          - n_speakers (float): number of speakers
class_docstr_tok: ["Implementation","of","the","Softmax","loss","as","defined","in","https",":","\/\/arxiv.org\/abs\/2003.11982","Args",":","-","embedding_dim","(","float",")",":","speaker","embedding","dim","-","n_speakers","(","float",")",":","number","of","speakers"]
code: def __init__(self, embedding_dim, n_speakers):
      super().__init__()
  
      self.criterion = torch.nn.CrossEntropyLoss()
      self.fc = nn.Linear(embedding_dim, n_speakers)
  
      print("Initialised Softmax Loss")
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new SoftmaxLoss object from inherited base classes: nn with example: obj = SoftmaxLoss(embedding_dim, n_speakers)