code: def process_inference_input(input_file):
      with io.open(input_file) as fp:
          texts = [line.strip() for line in fp.readlines()]
      tokenizer = SpacyInstance(disable=["tagger", "parser", "ner"])
      examples = []
      for i, t in enumerate(texts):
          examples.append(
              TokenClsInputExample(str(i), t, tokenizer.tokenize(t))
          )
      return examples
  
imports: import argparse
  import io
  import logging
  import os
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from nlp_architect.data.sequential_tagging import TokenClsInputExample, TokenClsProcessor
  from nlp_architect.data.utils import write_column_tagged_file
  from nlp_architect.models.transformers import TransformerTokenClassifier
  from nlp_architect.nn.torch import setup_backend, set_seed
  from nlp_architect.procedures.procedure import Procedure
  from nlp_architect.procedures.registry import register_inference_cmd, register_train_cmd
  from nlp_architect.procedures.transformers.base import create_base_args, inference_args, train_args
  from nlp_architect.utils.io import prepare_output_path
  from nlp_architect.utils.text import SpacyInstance
label_desc: Use image node_id 3 for calling a global function with example usage: process_inference_input(input_file) and returns: examples