code: def __init__(self, config: MptConfig):
      super().__init__()
      hidden_size = config.hidden_size
  
      self.up_proj = nn.Linear(hidden_size, 4 * hidden_size, bias=False)
      self.act = nn.GELU(approximate="none")
      self.down_proj = nn.Linear(
          4 * hidden_size, hidden_size, bias=False
      )
      self.hidden_dropout = config.attn_config.attn_pdrop
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
  from torch.nn import functional
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_mpt import MptConfig
label_desc: Use image node_id 1 to create a new MptMLP object from inherited base classes: nn with example: obj = MptMLP(config)