code: def __init__(
      self, num_embeddings: int, embedding_dim: int, padding_idx: int
  ):
      super().__init__()
      padding_mult = torch.ones((num_embeddings, 1), dtype=torch.int64)
      padding_mult[padding_idx] = 0
      self.register_buffer("padding_mult", padding_mult)
      self.embeddings = nn.parameter.Parameter(
          initialize_embeddings((num_embeddings, embedding_dim))
      )
  
imports: import math
  from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from TTS.tts.layers.delightful_tts.conv_layers import ConvNorm
label_desc: Use image node_id 1 to create a new EmbeddingPadded object from inherited base classes: nn with example: obj = EmbeddingPadded(num_embeddings, embedding_dim, padding_idx)