class_docstr: Positional encoding relying on a fourier kernel matching the one used in the
  "Attention is all of Need" paper. The implementation builds on DeTR code
  https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
class_docstr_tok: ["Positional","encoding","relying","on","a","fourier","kernel","matching","the","one","used","in","the","''","Attention","is","all","of","Need","''","paper",".","The","implementation","builds","on","DeTR","code","https",":","\/\/github.com\/facebookresearch\/detr\/blob\/master\/models\/position_encoding.py"]
code: def __init__(self, hidden_dim=32, dim=768, temperature=10000):
      super().__init__()
      self.token_projection = nn.Conv2d(
          hidden_dim * 2, dim, kernel_size=1
      )
      self.scale = 2 * math.pi
      self.temperature = temperature
      self.hidden_dim = hidden_dim
      self.dim = dim
  
imports: import logging
  import math
  from functools import partial
  from typing import List, Optional
  import torch
  import torch.nn
  from fairscale.nn import checkpoint_wrapper
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.models.trunks.vision_transformer import Mlp
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.misc import set_torch_seed
label_desc: Use image node_id 1 to create a new PositionalEncodingFourier object from inherited base classes: nn with example: obj = PositionalEncodingFourier(hidden_dim, dim, temperature)