class_docstr: This is a more standard version of the position embedding, very similar to the one
  used by the Attention is all you need paper, generalized to work on images.
code: def __init__(
      self,
      num_pos_feats=64,
      temperature=10000,
      normalize=False,
      scale=None,
  ):
      super().__init__()
      self.num_pos_feats = num_pos_feats
      self.temperature = temperature
      self.normalize = normalize
      if scale is not None and normalize is False:
          raise ValueError(
              "normalize should be True if scale is passed"
          )
      if scale is None:
          scale = 2 * math.pi
      self.scale = scale
  
imports: import math
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  import torchvision
  from mmf.models.unit.misc import NestedTensor
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from torchvision.ops.misc import FrozenBatchNorm2d
label_desc: Use image node_id 1 to create a new PositionEmbeddingSine object from inherited base classes: nn with example: obj = PositionEmbeddingSine(num_pos_feats, temperature, normalize, scale)