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.
  
  Adapted from https://github.com/shannanyinxiang/SPTS.
class_docstr_tok: ["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",".","Adapted","from","https",":","\/\/github.com\/shannanyinxiang\/SPTS","."]
code: def forward(self, mask: Tensor):
      assert mask is not None
      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=mask.device
      )
      dim_t = self.temperature ** (
          2
          * torch.div(dim_t, 2, rounding_mode="floor")
          / 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
  import torch
  from torch import Tensor, nn
label_desc: Use image node_id 2 for calling the PositionEmbeddingSine obj's underlying member method code with example usage: obj.forward(mask) and returns: pos