code: def prepare_examples(tokenizer, len_dataset=1000):
      sentences = [
          "Mars is the fourth planet from the Sun.",
          "has a crust primarily composed of elements",
          "However, it is unknown",
          "can be viewed from Earth",
          "It was the Romans",
      ]
      texts = []
      for _ in range(len_dataset):
          n_times = random.randint(1, 30)
          texts.append(
              " ".join(random.choice(sentences) for _ in range(n_times))
          )
      encoded_inputs = [
          tokenizer(text, return_tensors="pt", truncation=True).to(
              device
          )
          for text in texts
      ]
      len(encoded_inputs), encoded_inputs[0].keys()
      fake_input_id = torch.LongTensor(
          per_gpu_eval_batch_size, max_seq_length
      )
      fake_input_id.fill_(1)
      fake_input_id = fake_input_id.to(device)
      fake_mask = torch.ones(
          per_gpu_eval_batch_size, max_seq_length
      ).to(device)
      fake_type_id = fake_input_id.clone().detach()
      if data_type == "fp16":
          fake_mask = fake_mask.half()
      elif data_type == "bf16":
          fake_mask = fake_mask.bfloat16()
      return encoded_inputs, fake_input_id, fake_mask, fake_type_id
  
file_path: nebuly/optimization/speedster/notebooks/huggingface/faster_transformer_bert.py
imports: import logging
  import random
  import time
  import speedster
  import torch
  from speedster import optimize_model
  from nebullvm.operations.optimizations.compilers.faster_transformer.bert import detect_and_swap_bert_model
  from nebullvm.operations.optimizations.compilers.utils import get_faster_transformer_repo_path
  from transformers import BertTokenizer
  from transformers.models.bert.modeling_bert import BertForSequenceClassification
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_examples(tokenizer, len_dataset) and returns: encoded_inputs,  fake_input_id,  fake_mask,  fake_type_id