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.
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","."]
code: def forward(self, tensor):
      # input shape is B x 2048 x 7 x 7 or B x 2048 x 1 x 1
      x = tensor
      not_mask = torch.ones((x.shape[0], *x.shape[2:]), device=x.device)
      y_embed = not_mask.cumsum(1, dtype=torch.float32)
      x_embed = not_mask.cumsum(2, dtype=torch.float32)
      if self.normalize:
          y_embed = (
              y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
          )
          x_embed = (
              x_embed / (x_embed[:, :, -1:] + self.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 copy import deepcopy
  from dataclasses import dataclass
  from typing import Any, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.encoders import IdentityEncoder
  from mmf.modules.layers import AttnPool1d
  from mmf.utils.build import build_classifier_layer, build_image_encoder, build_text_encoder
  from mmf.utils.general import filter_grads
  from mmf.utils.modeling import get_bert_configured_parameters
  from mmf.utils.transform import transform_to_batch_sequence
  from omegaconf import MISSING
label_desc: Use image node_id 2 for calling the PositionEmbeddingSine obj's underlying member method code with example usage: obj.forward(tensor) and returns: pos