code: def __init__(self, config: MptConfig):
      super().__init__(config)
      self.transformer = MptModel(config)
      self.lm_head = nn.Linear(
          config.hidden_size, config.vocab_size, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
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 MptForCausalLM object from inherited base classes: MptPreTrainedModel with example: obj = MptForCausalLM(config)