code: def __init__(self, demb):
      super(PositionalEmbedding, self).__init__()
      self.demb = demb
      inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
      self.register_buffer("inv_freq", inv_freq)
  
imports: from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.collections.tts.modules.submodules import ConditionalInput, ConditionalLayerNorm, LinearNorm
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, adapter_mixins, typecheck
  from nemo.core.neural_types.elements import EncodedRepresentation, LengthsType, MaskType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new PositionalEmbedding object from inherited base classes: nn with example: obj = PositionalEmbedding(demb)