code: def forward(self, x: torch.Tensor):
      pe = (
          self.positional_encodings[: x.shape[0]]
          .detach()
          .requires_grad_(False)
      )
      x = x + pe
      x = self.dropout(x)
      return x
  
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 2 for calling the PositionalEncoding obj's underlying member method code with example usage: obj.forward(x) and returns: x