code: def _embed(
      self, input: torch.Tensor, weight: torch.Tensor
  ) -> torch.Tensor:
      base_layer = self.get_base_layer()
      return F.embedding(
          input,
          weight,
          padding_idx=base_layer.padding_idx,
          max_norm=base_layer.max_norm,
          norm_type=base_layer.norm_type,
          scale_grad_by_freq=base_layer.scale_grad_by_freq,
          sparse=base_layer.sparse,
      )
  
imports: import math
  import warnings
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.pytorch_utils import Conv1D
  from peft.tuners.tuners_utils import BaseTunerLayer
  from peft.utils.other import transpose
  from .config import LoraConfig
label_desc: Use image node_id 6 for calling the Embedding obj's underlying member method code with example usage: obj._embed(input, weight) and returns: F