title: Loaded model with joblib accepts any number of input
body: <p>I have a model, that accepts an embedding of 768 size. I saved it using joblib like</p>
  <pre><code>from joblib import dump
  dump(self.model,'textclassifier/saved_model/text_classifier.joblib') 
  </code></pre>
  <p>Now if I load the model using joblib</p>
  <pre><code>from joblib import load
  model = load(model_path) 
  print(model)
  </code></pre>
  <p>The output is</p>
  <pre><code>OneVsRestClassifier(estimator=XGBClassifier(base_score=None, booster=None,
                                              colsample_bylevel=None,
                                              colsample_bynode=None,
                                              colsample_bytree=None,
                                              enable_categorical=False,
                                              gamma=None, gpu_id=None,
                                              importance_type=None,
                                              interaction_constraints=None,
                                              learning_rate=None,
                                              max_delta_step=None, max_depth=4,
                                              min_child_weight=None, missing=nan,
                                              monotone_constraints=None,
                                              n_estimators=100, n_jobs=-1,
                                              num_parallel_tree=None,
                                              predictor=None, random_state=None,
                                              reg_alpha=None, reg_lambda=None,
                                              scale_pos_weight=None,
                                              subsample=None, tree_method=None,
                                              validate_parameters=None,
                                              verbosity=None))
  </code></pre>
  <p>Now it is accepting any number of embeddings.</p>
  <pre><code>embeddings = Embedder().results([sentence])
  results = model.predict(embeddings[0:10])
  print(results)
  </code></pre>
  <p>The output of results seems to be in correct dimensions, but model.predict is taking any number of embeddings which should raise</p>
  <pre><code>ValueError: Number of features of the model must match the input. Model n_features is 10 and input n_features is 786.
  </code></pre>
  <p>But it is not. What is the reason?</p>