class_docstr: Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
      Accepts an input of size (N, M, D)
          where N is the number of speakers in the batch,
          M is the number of utterances per speaker,
          and D is the dimensionality of the embedding vector
      Args:
          - init_w (float): defines the initial value of w
          - init_b (float): definies the initial value of b
code: def __init__(self, init_w=10.0, init_b=-5.0):
      super(AngleProtoLoss, self).__init__()
      # pylint: disable=E1102
      self.w = nn.Parameter(torch.tensor(init_w))
      # pylint: disable=E1102
      self.b = nn.Parameter(torch.tensor(init_b))
      self.criterion = torch.nn.CrossEntropyLoss()
  
      print(" > Initialised Angular Prototypical loss")
  
file_path: TTS/TTS/speaker_encoder/losses.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 1 to create a new AngleProtoLoss object from inherited base classes: nn with example: obj = AngleProtoLoss(init_w, init_b)