class_docstr: Main neural network of the outputnet
  
  Note: Do not put dropout layers here, the model will not converge.
  
  Args:
          outputnet_size (List[int]): the architecture of the parameter model
          input_size (int): size of input for the first layer
          output_size (int): size of output i.e size of the feature dim
          frame_channels (int): feature dim to set the flat start bias
          flat_start_params (dict): flat start parameters to set the bias
class_docstr_tok: ["Main","neural","network","of","the","outputnet","Note",":","Do","not","put","dropout","layers","here",",","the","model","will","not","converge",".","Args",":","outputnet_size","(","List","[","int","]",")",":","the","architecture","of","the","parameter","model","input_size","(","int",")",":","size","of","input","for","the","first","layer","output_size","(","int",")",":","size","of","output","i.e","size","of","the","feature","dim","frame_channels","(","int",")",":","feature","dim","to","set","the","flat","start","bias","flat_start_params","(","dict",")",":","flat","start","parameters","to","set","the","bias"]
code: def __init__(
      self,
      outputnet_size: List[int],
      input_size: int,
      output_size: int,
      frame_channels: int,
      flat_start_params: dict,
  ):
      super().__init__()
      self.frame_channels = frame_channels
  
      self.layers = nn.ModuleList(
          [
              Linear(inp, out)
              for inp, out in zip(
                  [input_size] + outputnet_size[:-1], outputnet_size
              )
          ]
      )
      self.last_layer = nn.Linear(outputnet_size[-1], output_size)
      self.flat_start_output_layer(
          flat_start_params["mean"],
          flat_start_params["std"],
          flat_start_params["transition_p"],
      )
  
imports: from typing import List, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from tqdm.auto import tqdm
  from TTS.tts.layers.tacotron.common_layers import Linear
  from TTS.tts.layers.tacotron.tacotron2 import ConvBNBlock
label_desc: Use image node_id 1 to create a new ParameterModel object from inherited base classes: nn with example: obj = ParameterModel(outputnet_size, input_size, output_size, frame_channels, flat_start_params)