code: def __init__(
      self, d_model: int, dropout_prob: float, max_len: int = 5000
  ):
      super().__init__()
      self.dropout = nn.Dropout(dropout_prob)
  
      self.register_buffer(
          "positional_encodings",
          get_positional_encoding(d_model, max_len),
          False,
      )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/positional_encoding.py
imports: import math
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new PositionalEncoding object from inherited base classes: nn with example: obj = PositionalEncoding(d_model, dropout_prob, max_len)