code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      seq_len = x.shape[1]
      pos = (
          torch.arange(0, seq_len, device=x.device, dtype=torch.float32)
          .unsqueeze(1)
          .repeat(1, self.dim_model)
      )
      dim = (
          torch.arange(
              0, self.dim_model, device=x.device, dtype=torch.float32
          )
          .unsqueeze(0)
          .repeat(seq_len, 1)
      )
      div = torch.exp(
          -math.log(10000) * (2 * (dim // 2) / self.dim_model)
      )
      pos *= div
      pos[:, 0::2] = torch.sin(pos[:, 0::2])
      pos[:, 1::2] = torch.cos(pos[:, 1::2])
  
      output = x.unsqueeze(-1) if x.ndim == 2 else x
  
      return output + pos.unsqueeze(0)
  
imports: import math
  import torch
  from xformers.components.positional_embedding import PositionEmbedding, PositionEmbeddingConfig, register_positional_embedding
label_desc: Use image node_id 2 for calling the SinePositionalEmbedding obj's underlying member method code with example usage: obj.forward(x) and returns: unknown