class_docstr: The implementation of relative position encoding for 2D image feature maps.
  
  Used in :class:`RelativePositionSelfAttention`.
code: def __init__(self, head_embed_dim: int, length: int = 14) -> None:
      super().__init__()
      self.head_embed_dim = head_embed_dim
      self.length = length
      self.embeddings_table_v = nn.Parameter(
          torch.randn(length * 2 + 2, head_embed_dim)
      )
      self.embeddings_table_h = nn.Parameter(
          torch.randn(length * 2 + 2, head_embed_dim)
      )
  
      nn.init.trunc_normal_(self.embeddings_table_v, std=0.02)
      nn.init.trunc_normal_(self.embeddings_table_h, std=0.02)
  
imports: from __future__ import annotations
  from typing import Tuple, cast, Any, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  import nni
  from nni.mutable import Categorical, MutableExpression, frozen_context, ensure_frozen, Mutable
  from nni.nas.nn.pytorch import ParametrizedModule, ModelSpace, MutableModule, Repeat, MutableConv2d, MutableLinear, MutableLayerNorm
  from nni.nas.space import current_model
  from nni.nas.oneshot.pytorch.supermodule.operation import MixedOperation
  from nni.nas.profiler.pytorch.flops import FlopsResult
  from nni.nas.profiler.pytorch.utils import MutableShape, ShapeTensor, profiler_leaf_module
  from .utils.pretrained import load_pretrained_weight
  from .utils.nn import DropPath
label_desc: Use image node_id 1 to create a new RelativePosition2D object from inherited base classes: nn with example: obj = RelativePosition2D(head_embed_dim, length)