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 forward(self, tensor_list: NestedTensor):
      x = tensor_list.tensors
      mask = tensor_list.mask
      not_mask = ~mask
      y_embed = not_mask.cumsum(1, dtype=torch.float32)
      x_embed = not_mask.cumsum(2, dtype=torch.float32)
      if self.normalize:
          eps = 1e-6
          y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
          x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
  
      dim_t = torch.arange(
          self.num_pos_feats, dtype=torch.float32, device=x.device
      )
      dim_t = self.temperature ** (
          2 * (dim_t // 2) / self.num_pos_feats
      )
  
      pos_x = x_embed[:, :, :, None] / dim_t
      pos_y = y_embed[:, :, :, None] / dim_t
      pos_x = torch.stack(
          (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
          dim=4,
      ).flatten(3)
      pos_y = torch.stack(
          (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
          dim=4,
      ).flatten(3)
      pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
      return pos
  
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 2 for calling the PositionEmbeddingSine obj's underlying member method code with example usage: obj.forward(tensor_list) and returns: pos