class_docstr: Implementation of the Softmax AnglePrototypical loss as defined in https://arxiv.org/abs/2009.14153
      Args:
          - embedding_dim (float): speaker embedding dim
          - n_speakers (float): number of speakers
          - init_w (float): defines the initial value of w
          - init_b (float): definies the initial value of b
class_docstr_tok: ["Implementation","of","the","Softmax","AnglePrototypical","loss","as","defined","in","https",":","\/\/arxiv.org\/abs\/2009.14153","Args",":","-","embedding_dim","(","float",")",":","speaker","embedding","dim","-","n_speakers","(","float",")",":","number","of","speakers","-","init_w","(","float",")",":","defines","the","initial","value","of","w","-","init_b","(","float",")",":","definies","the","initial","value","of","b"]
docstr: Calculates the SoftmaxAnglePrototypical loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
docstr_tok: ["Calculates","the","SoftmaxAnglePrototypical","loss","for","an","input","of","dimensions","(","num_speakers",",","num_utts_per_speaker",",","dvec_feats",")"]
code: def forward(self, x, label=None):
      """
      Calculates the SoftmaxAnglePrototypical loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
      """
  
      Lp = self.angleproto(x)
  
      Ls = self.softmax(x, label)
  
      return Ls + Lp
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the SoftmaxAngleProtoLoss obj's underlying member method code with example usage: obj.forward(x, label) and returns: unknown