code: def __init__(self, config: MptConfig):
      super().__init__(config)
  
      self.hidden_size = config.hidden_size
      self.num_heads = config.n_heads
  
      # Embedding + LN Embedding
      self.wte = nn.Embedding(config.vocab_size, self.hidden_size)
  
      # Transformer blocks
      self.blocks = nn.ModuleList(
          [MptBlock(config) for _ in range(config.n_layers)]
      )
  
      # Final Layer Norm
      self.norm_f = LayerNorm(
          self.hidden_size, eps=config.layer_norm_epsilon
      )
      # backward compatibility with weights on the Hub
      self.norm_f.bias = None
  
      self.gradient_checkpointing = 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 MptModel object from inherited base classes: MptPreTrainedModel with example: obj = MptModel(config)