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 inference(self, embedding):
      x = self.fc(embedding)
      activations = torch.nn.functional.softmax(x, dim=1).squeeze(0)
      class_id = torch.argmax(activations)
      return class_id
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 3 for calling the SoftmaxLoss obj's underlying member method code with example usage: obj.inference(embedding) and returns: class_id