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"]
code: def __init__(
      self, embedding_dim, n_speakers, init_w=10.0, init_b=-5.0
  ):
      super().__init__()
  
      self.softmax = SoftmaxLoss(embedding_dim, n_speakers)
      self.angleproto = AngleProtoLoss(init_w, init_b)
  
      print("Initialised SoftmaxAnglePrototypical Loss")
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new SoftmaxAngleProtoLoss object from inherited base classes: nn with example: obj = SoftmaxAngleProtoLoss(embedding_dim, n_speakers, init_w, init_b)