class_docstr: Tacotron2 decoder. We don't use Zoneout but Dropout between RNN layers.
  
  Args:
      in_channels (int): number of input channels.
      frame_channels (int): number of feature frame channels.
      r (int): number of outputs per time step (reduction rate).
      memory_size (int): size of the past window. if <= 0 memory_size = r
      attn_type (string): type of attention used in decoder.
      attn_win (bool): if true, define an attention window centered to maximum
          attention response. It provides more robust attention alignment especially
          at interence time.
      attn_norm (string): attention normalization function. 'sigmoid' or 'softmax'.
      prenet_type (string): 'original' or 'bn'.
      prenet_dropout (float): prenet dropout rate.
      forward_attn (bool): if true, use forward attention method. https://arxiv.org/abs/1807.06736
      trans_agent (bool): if true, use transition agent. https://arxiv.org/abs/1807.06736
      forward_attn_mask (bool): if true, mask attention values smaller than a threshold.
      location_attn (bool): if true, use location sensitive attention.
      attn_K (int): number of attention heads for GravesAttention.
      separate_stopnet (bool): if true, detach stopnet input to prevent gradient flow.
code: def _update_memory(self, memory):
      if len(memory.shape) == 2:
          return memory[:, self.frame_channels * (self.r - 1) :]
      return memory[:, :, self.frame_channels * (self.r - 1) :]
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from .common_layers import Prenet, Linear
  from .attentions import init_attn
label_desc: Use image node_id 7 for calling the Decoder obj's underlying member method code with example usage: obj._update_memory(memory) and returns: memory, memory