code: def __init__(self, config, weights):
      super().__init__(config)
      self.transformer = BloomModel(config, weights)
  
      self.lm_head = TensorParallelHead.load(
          config,
          prefix="word_embeddings",
          weights=weights,
      )
  
imports: import math
  import os
  import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.distributed
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import LayerNorm
  from torch.nn import functional
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
  from transformers import BloomConfig, PreTrainedModel
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 1 to create a new BloomForCausalLM object from inherited base classes: BloomPreTrainedModel with example: obj = BloomForCausalLM(config, weights)