class_docstr: Fixed positional encoding (embedding layer) from sine and cosine functions
  of different frequencies according to https://arxiv.org/abs/1706.03762
  
  Args:
      hidden_size: size of the embeddings in the model, also known as d_model
      max_sequence_length: maximum allowed length of the input sequence
class_docstr_tok: ["Fixed","positional","encoding","(","embedding","layer",")","from","sine","and","cosine","functions","of","different","frequencies","according","to","https",":","\/\/arxiv.org\/abs\/1706.03762","Args",":","hidden_size",":","size","of","the","embeddings","in","the","model",",","also","known","as","d_model","max_sequence_length",":","maximum","allowed","length","of","the","input","sequence"]
code: def __init__(self, hidden_size, max_sequence_length=512):
      super().__init__()
  
      self._hidden_size = hidden_size
      self._max_sequence_length = max_sequence_length
      self._build_pos_enc(
          hidden_size=self._hidden_size,
          max_sequence_length=self._max_sequence_length,
      )
  
imports: import math
  import numpy
  import torch
  from torch import nn
  from torch.nn.functional import gelu
  from nemo.collections.common.parts import form_attention_mask
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new FixedPositionalEncoding object from inherited base classes: nn with example: obj = FixedPositionalEncoding(hidden_size, max_sequence_length)