code: def forward(
      self,
      q: torch.Tensor,
      k: torch.Tensor,
      v: torch.Tensor,
      *args,
      **kwargs,
  ):
      # Handle a smaller dimension than expected
      padding = 0
      if q.shape[1] < self.seq_len:
          padding = self.seq_len - q.shape[1]
          pad_dims = (0, 0, 0, padding)
          q = torch.nn.functional.pad(q, pad_dims)
          k = torch.nn.functional.pad(k, pad_dims)
          v = torch.nn.functional.pad(v, pad_dims)
  
      k_projected = self.E(k.transpose(-2, -1)).transpose(-2, -1)
      v_projected = self.F(v.transpose(-2, -1)).transpose(-2, -1)
  
      y = scaled_dot_product_attention(
          q=q,
          k=k_projected,
          v=v_projected,
          att_mask=None,
          dropout=self.attn_drop,
      )
  
      y = self.attn_drop(y)
  
      return y[:, :-padding, :] if padding > 0 else y
  
file_path: xformers/xformers/components/attention/linformer.py
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from xformers.components.attention import Attention, AttentionConfig, register_attention
  from xformers.components.attention.core import scaled_dot_product_attention
label_desc: Use image node_id 2 for calling the LinformerAttention obj's underlying member method code with example usage: obj.forward(q, k, v) and returns: unknown