docstr: Method converts array with text into one-dimensional list
  
  :param array_with_text: numpy array or list with text data
  :return features_list: one-dimensional list with text
docstr_tok: ["Method","converts","array","with","text","into","one-dimensional","list",":","param","array_with_text",":","numpy","array","or","list","with","text","data",":","return","features_list",":","one-dimensional","list","with","text"]
code: def _convert_to_one_dim(array_with_text):
      """Method converts array with text into one-dimensional list
  
      :param array_with_text: numpy array or list with text data
      :return features_list: one-dimensional list with text
      """
      features = np.ravel(np.array(array_with_text, dtype=str))
      features_list = list(features)
      return features_list
  
imports: import warnings
  from typing import Optional
  import numpy
  from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.data_operations.text_preprocessing import TextCleanImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.text_pretrained import PretrainedEmbeddingsImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 4 for calling the SkLearnTextVectorizeStrategy obj's underlying member method code with example usage: obj._convert_to_one_dim(array_with_text) and returns: features_list