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 forward(self, x, label=None):
      # reshape for compatibility
      x = x.reshape(-1, x.size()[-1])
      label = label.reshape(-1)
  
      x = self.fc(x)
      L = self.criterion(x, label)
  
      return L
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the SoftmaxLoss obj's underlying member method code with example usage: obj.forward(x, label) and returns: L