code: def pad_energy_avg_and_f0(energy_avg, f0, max_out_len):
      to_pad = int(max_out_len - energy_avg.shape[1])
      if to_pad > 0:
          f0 = F.pad(f0[None], [0, to_pad])[0]
          energy_avg = F.pad(energy_avg[None], [0, to_pad])[0]
      to_pad = int(max_out_len - f0.shape[1])
      if to_pad > 0:
          f0 = F.pad(f0[None], [0, to_pad])[0]
      return energy_avg, f0
  
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 2 for calling a global function with example usage: pad_energy_avg_and_f0(energy_avg, f0, max_out_len) and returns: energy_avg,  f0