class_docstr: Takes model parameters (modelConfig) from config file to initialize radtts module.
  Specify the type of training in the include_modules parameter. "decatnvpred" for decoder training. and "decatnunvbiasdpmvpredapm" for feature training
  n_speakers (int): Number of speakers
  n_speaker_dim (int): number of speakers dimension
  n_text (int): Symbols embedding size
  n_text_dim (int):
  n_flows (int):
  n_conv_layers_per_step (int): number of convolution layers per step
  dummy_speaker_embedding (bool):
  include_modules (string): A string that describes what to train. "decatnvpred" for decoder training. and "decatnunvbiasdpmvpredapm" for feature training.
  scaling_fn (string): scaling function
  decoder_use_partial_padding (Bool): Set this to True to add partial padding
  learn_alignments (Bool): set this to true to learn alignments
  attn_use_CTC (Bool): set True to use CTC
  n_f0_dims (int): number of Pitch dimension
  n_early_size (int):
  n_early_every (int):
  n_group_size (int):
  decoder_use_unvoiced_bias (bool):
  context_lstm_w_f0_and_energy (bool):
  use_first_order_features (bool):
  ap_pred_log_f0 (bool):
  dur_model_config: model configuration for duration
  f0_model_config: model configuration for Pitch
  energy_model_config: model configuration for energy
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docstr: feats: b x max_length
  out_lens: b-dim
docstr_tok: ["feats",":","b","x","max_length","out_lens",":","b-dim"]
code: def get_first_order_features(self, feats, dilation=1):
      """
      feats: b x max_length
      out_lens: b-dim
      """
      # add an extra column
      feats_extended_R = torch.cat(
          (feats, torch.zeros_like(feats[:, 0:dilation])), dim=1
      )
      feats_extended_L = torch.cat(
          (torch.zeros_like(feats[:, 0:dilation]), feats), dim=1
      )
      dfeats_R = feats_extended_R[:, dilation:] - feats
      dfeats_L = feats - feats_extended_L[:, 0:-dilation]
  
      return (dfeats_R + dfeats_L) * 0.5
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from nemo.collections.tts.modules.attribute_prediction_model import get_attribute_prediction_model
  from nemo.collections.tts.modules.common import AffineTransformationLayer, BiLSTM, ConvAttention, ExponentialClass, Invertible1x1Conv, Invertible1x1ConvLUS, LinearNorm, get_radtts_encoder
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths, mas_width1, regulate_len
  from nemo.core.classes import Exportable, NeuralModule
  from nemo.core.neural_types.elements import Index, LengthsType, MelSpectrogramType, TokenDurationType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 8 for calling the RadTTSModule obj's underlying member method code with example usage: obj.get_first_order_features(feats, dilation) and returns: unknown