class_docstr: TransformerEncoderLayer class.
  Args:
      d_model (int): dimension of model
      nhead (int): number of head
      dim_feedforward (int): dimension of feedforward
      dropout (float): dropout
      activation (str): activation
class_docstr_tok: ["TransformerEncoderLayer","class",".","Args",":","d_model","(","int",")",":","dimension","of","model","nhead","(","int",")",":","number","of","head","dim_feedforward","(","int",")",":","dimension","of","feedforward","dropout","(","float",")",":","dropout","activation","(","str",")",":","activation"]
code: def with_pos_embed(
      self, tensor, pos: Optional[Tensor], num_zeros=None
  ):
      if num_zeros is not None:
          return (
              tensor
              if pos is None
              else torch.cat(
                  [tensor[:num_zeros], (tensor[num_zeros:] + pos)],
                  dim=0,
              )
          )
      else:
          return tensor if pos is None else tensor + pos
  
imports: import copy
  import torch
  import torch.nn.functional
  from typing import Optional
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the TransformerEncoderLayer obj's underlying member method code with example usage: obj.with_pos_embed(tensor, pos, num_zeros) and returns: unknown, unknown