class_docstr: Integration with the spaCy NLP library.
class_docstr_tok: ["Integration","with","the","spaCy","NLP","library","."]
code: def predict(self, df, args=None):
      config = self.model_storage.json_get("config")
      model_args = self.model_storage.json_get("model_args")
      bytes_data = self.model_storage.file_get("model_data")
  
      # Deserialize the model using the provided config
      lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
      nlp = lang_cls.from_config(config)
      nlp.from_bytes(bytes_data)
  
      column_name = model_args["target_column"]
      linguistic_feature = model_args.get("linguistic_feature")
  
      predictions_whole = []
      predictions_attributes = []
      for _, text in df.iterrows():
          doc = nlp(text[column_name])
          if linguistic_feature in lingustic_features:
              pred_whole, pred_attributes = lingustic_features[
                  linguistic_feature
              ](doc)
              predictions_whole.append(pred_whole)
              predictions_attributes.append(pred_attributes)
  
      predictions_df = pd.DataFrame(predictions_attributes)
      df = pd.concat([df, predictions_df], axis=1)
      df[model_args["target"]] = predictions_whole
  
      return df
  
file_path: mindsdb/mindsdb/integrations/handlers/spacy_handler/spacy_handler.py
imports: import spacy
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from typing import Optional
label_desc: Use image node_id 4 for calling the SpacyHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: df