code: def test_load_from_state_dict(
      self, encoder_config: LLMEncoderConfig, wrap: bool
  ):
      def weights_init(m):
          """Reinitialize the weights of a torch module."""
          if hasattr(m, "weight") and m.weight.ndim > 1:
              torch.nn.init.xavier_uniform_(m.weight.data)
  
      # Create two encoders from the same config
      encoder1 = LLMEncoder(encoder_config=encoder_config)
      encoder2 = LLMEncoder(encoder_config=encoder_config)
  
      if wrap:
          encoder1 = WrapperModule(encoder1)
          encoder2 = WrapperModule(encoder2)
  
      # Reinitialize the weights of one encoder so the two are not identical
      encoder2.apply(weights_init)
  
      # Ensure that the weights are different
      encoder1_sd = encoder1.state_dict()
      encoder2_sd = encoder2.state_dict()
      assert any(
          map(
              lambda k: not torch.equal(encoder1_sd[k], encoder2_sd[k]),
              encoder1_sd.keys(),
          )
      )
  
      # Load the weights of encoder1 back into encoder2 and ensure the weights are equal
      encoder2.load_state_dict(encoder1_sd)
      encoder2_sd = encoder2.state_dict()
      assert all(
          map(
              lambda k: torch.equal(encoder1_sd[k], encoder2_sd[k]),
              encoder1_sd.keys(),
          )
      )
  
imports: import copy
  import pytest
  import torch
  import torch.nn
  from transformers import AutoConfig, PreTrainedModel
  from ludwig.encoders.text_encoders import LLMEncoder
  from ludwig.schema.encoders.text_encoders import LLMEncoderConfig
  from ludwig.schema.llms.peft import BaseAdapterConfig, LoraConfig
  from ludwig.utils.llm_utils import get_context_len
label_desc: Use image node_id 7 for calling the TestLLMEncoder obj's underlying member method code with example usage: obj.test_load_from_state_dict(encoder_config, wrap) without return types