docstr: ```
  load text files from csv file
  CSV should have at least two columns.
  Example:
  Text               | Label
  I love this movie. | positive
  I hated this movie.| negative
  
  
  Args:
    csv_filepath(str): path to CSV file
    text_column(str): name of column containing the texts. default:'text'
    label_column(str): name of column containing the labels in string format
                       default:'label'
    sep(str): character that separates columns in CSV. default:','
    encoding(str): encoding to use. default:None (auto-detected)
  Returns:
    tuple: (texts, labels, label_names)
  ```
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code: def load_texts_from_csv(
      cls,
      csv_filepath,
      text_column="text",
      label_column="label",
      sep=",",
      encoding=None,
  ):
      """
      ```
      load text files from csv file
      CSV should have at least two columns.
      Example:
      Text               | Label
      I love this movie. | positive
      I hated this movie.| negative
  
  
      Args:
        csv_filepath(str): path to CSV file
        text_column(str): name of column containing the texts. default:'text'
        label_column(str): name of column containing the labels in string format
                           default:'label'
        sep(str): character that separates columns in CSV. default:','
        encoding(str): encoding to use. default:None (auto-detected)
      Returns:
        tuple: (texts, labels, label_names)
      ```
      """
      if encoding is None:
          with open(csv_filepath, "rb") as f:
              encoding = U.detect_encoding([f.read()])
              if encoding != "utf-8":
                  print(
                      "detected encoding: %s (if wrong, set manually)"
                      % (encoding)
                  )
      import pandas as pd
  
      df = pd.read_csv(csv_filepath, encoding=encoding, sep=sep)
      texts = df[text_column].fillna("fillna").values
      labels = df[label_column].values
      le = LabelEncoder()
      le.fit(labels)
      labels = le.transform(labels)
      return (texts, labels, le.classes_)
  
imports: from .None import utils
  from .imports import *
label_desc: Use image node_id 4 for calling the Classifier obj's underlying member method code with example usage: obj.load_texts_from_csv(cls, csv_filepath, text_column, label_column, sep, encoding) and returns: texts,  labels,  le