class_docstr: The goal here is to compare the embeddings generated by the model trained
  using Megatron-LM with the one from the transformers library, with a small GPT2-like model
  to ensure that the conversion from Megatron-LM to transformers has been done successfully.
  The script compares the logits of the embedding layer and the transformer layers.
  
  WARNING: It is expected that these logits will not have exactly the same statistics when running
  the code on CPU or GPU. For more info, please visit:
    - https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548
    - https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9
  
  
  You need to install tokenizers following this readme:
      - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
  
  Tokenizer used during training:
      - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
  
  # TODO change the script (or just add skip) when building the env with tokenizers 0.12.0
code: def setUp(self):
      super().setUp()
      self.path_bigscience_model = "bigscience/bigscience-small-testing"
  
imports: import math
  import unittest
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the BloomEmbeddingTest obj's underlying member method code with example usage: obj.setUp() without return types