class_docstr: Positional encoding using random spatial frequencies.
code: def __init__(
      self, num_pos_feats: int = 64, scale: Optional[float] = None
  ) -> None:
      super().__init__()
      if scale is None or scale <= 0.0:
          scale = 1.0
      self.register_buffer(
          "positional_encoding_gaussian_matrix",
          scale * torch.randn((2, num_pos_feats)),
      )
  
imports: from __future__ import annotations
  from math import pi
  from typing import Optional
  import torch
  from torch import nn
  from kornia.contrib.models.common import LayerNorm2d
  from kornia.core import Device, Module, Tensor, concatenate, cos, sin, stack, zeros
label_desc: Use image node_id 1 to create a new PositionEmbeddingRandom object from inherited base classes: Module with example: obj = PositionEmbeddingRandom(num_pos_feats, scale)