code: def __init__(
      self,
      hidden_size: int,
      intermediate_size: int,
      hidden_act: str,
  ):
      super().__init__()
      self.gate_proj = nn.Linear(
          hidden_size, intermediate_size, bias=False
      )
      self.down_proj = nn.Linear(
          intermediate_size, hidden_size, bias=False
      )
      self.up_proj = nn.Linear(
          hidden_size, intermediate_size, bias=False
      )
      self.act_fn = ACT2FN[hidden_act]
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.llama.configuration_llama import LlamaConfig
label_desc: Use image node_id 1 to create a new LlamaMLP object from inherited base classes: nn with example: obj = LlamaMLP(hidden_size, intermediate_size, hidden_act)