input: Write a function to implement a machine learning algorithm in Python to categorize a dataset of records. The function should train a Support Vector Classifier (SVC) on the given dataset and return the predicted category for a specified input features list.
  assert predict_category([
   {"label": "apple", "features": [1, 1, 0]}, 
   {"label": "banana", "features": [0, 1, 1]}, 
   {"label": "lemon", "features": [0, 0, 1]}, 
   {"label": "orange", "features": [1, 0, 0]}, 
   {"label": "pear", "features": [0, 1, 0]}, 
  ], [1, 0, 1]) == "banana"
  assert predict_category([
   {"label": "apple", "features": [1, 1, 0]}, 
   {"label": "banana", "features": [0, 1, 1]}, 
   {"label": "lemon", "features": [0, 0, 1]}, 
   {"label": "orange", "features": [1, 0, 0]}, 
   {"label": "pear", "features": [0, 1, 0]}, 
  ], [0, 1, 0]) == "pear"
  assert predict_category([
   {"label": "apple", "features": [1, 1, 0]}, 
   {"label": "banana", "features": [0, 1, 1]}, 
   {"label": "lemon", "features": [0, 0, 1]}, 
   {"label": "orange", "features": [1, 0, 0]}, 
   {"label": "pear", "features": [0, 1, 0]}, 
  ], [1, 0, 0]) == "orange"
output: def predict_category(dataset, input_features):
      from sklearn.svm import SVC
      import numpy as np
      
      X = np.array([record["features"] for record in dataset])
      y = np.array([record["label"] for record in dataset])
      
      svc = SVC()
      svc.fit(X, y)
      
      return svc.predict([input_features])[0]