docstr: Generates data parameters for flat starting the HMM.
  
  Args:
      data_loader (torch.utils.data.Dataloader): _description_
      out_channels (int): mel spectrogram channels
      states_per_phone (_type_): HMM states per phone
docstr_tok: ["Generates","data","parameters","for","flat","starting","the","HMM",".","Args",":","data_loader","(","torch.utils.data.Dataloader",")",":","_description_","out_channels","(","int",")",":","mel","spectrogram","channels","states_per_phone","(","_type_",")",":","HMM","states","per","phone"]
code: def get_data_parameters_for_flat_start(
      data_loader: torch.utils.data.DataLoader,
      out_channels: int,
      states_per_phone: int,
  ):
      """Generates data parameters for flat starting the HMM.
  
      Args:
          data_loader (torch.utils.data.Dataloader): _description_
          out_channels (int): mel spectrogram channels
          states_per_phone (_type_): HMM states per phone
      """
  
      # State related information for transition_p
      total_state_len = 0
      total_mel_len = 0
  
      # Useful for data mean an std
      total_mel_sum = 0
      total_mel_sq_sum = 0
  
      for batch in tqdm(data_loader, leave=False):
          text_lengths = batch["token_id_lengths"]
          mels = batch["mel"]
          mel_lengths = batch["mel_lengths"]
  
          total_state_len += torch.sum(text_lengths)
          total_mel_len += torch.sum(mel_lengths)
          total_mel_sum += torch.sum(mels)
          total_mel_sq_sum += torch.sum(torch.pow(mels, 2))
  
      data_mean = total_mel_sum / (total_mel_len * out_channels)
      data_std = torch.sqrt(
          (total_mel_sq_sum / (total_mel_len * out_channels))
          - torch.pow(data_mean, 2)
      )
      average_num_states = total_state_len / len(data_loader.dataset)
      average_mel_len = total_mel_len / len(data_loader.dataset)
      average_duration_each_state = average_mel_len / average_num_states
      init_transition_prob = 1 / average_duration_each_state
  
      return (
          data_mean,
          data_std,
          (init_transition_prob * states_per_phone),
      )
  
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 for calling the OverflowUtils obj's underlying member method code with example usage: obj.get_data_parameters_for_flat_start(data_loader, out_channels, states_per_phone) and returns: data_mean,  data_std,  unknown