code: def __init__(self, config: M2M100Config):
      super().__init__(config)
  
      padding_idx, vocab_size = config.pad_token_id, config.vocab_size
      self.shared = nn.Embedding(
          vocab_size, config.d_model, padding_idx
      )
  
      self.encoder = M2M100Encoder(config, self.shared)
      self.decoder = M2M100Decoder(config, self.shared)
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import math
  import random
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...deepspeed import is_deepspeed_zero3_enabled
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_m2m_100 import M2M100Config
label_desc: Use image node_id 1 to create a new M2M100Model object from inherited base classes: M2M100PreTrainedModel with example: obj = M2M100Model(config)