class_docstr: Fused MLP layer for incrementally improved training efficiency
class_docstr_tok: ["Fused","MLP","layer","for","incrementally","improved","training","efficiency"]
code: def forward(
      self, x: torch.Tensor
  ) -> torch.Tensor:  # pylint: disable=invalid-name
      return self.swiglu(x)
  
imports: import logging
  import warnings
  from functools import partial
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import transformers
  from einops import rearrange
  from flash_attn.bert_padding import pad_input, unpad_input
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from transformers.models.llama.modeling_llama import LlamaAttention
  from transformers.models.llama.modeling_llama import LlamaDecoderLayer
  from transformers.models.llama.modeling_llama import LlamaMLP, apply_rotary_pos_emb, repeat_kv
  from xformers.ops import SwiGLU
  from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
label_desc: Use image node_id 3 for calling the FusedMLP obj's underlying member method code with example usage: obj.forward(x) and returns: self