import numpy as np

def calculate(list) -> dict:
  try:
    if len(list) != 9:
      raise ValueError("List must contain nine numbers.")
    calculations = {}
    #create a 3x3 numpy array from the input list
    input_array = np.array([list[0:3],list[3:6],list[6:9]])
    #calc mean[axis1, axis2, flattened]
    calculations['mean'] = calculations.get('mean',[input_array.mean(axis=0).tolist(),input_array.mean(axis=1).tolist(),input_array.flatten().mean()])
    #calc variance[axis1, axis2, flattened],
    calculations['variance']= calculations.get('variance', [input_array.var(axis=0).tolist(),input_array.var(axis=1).tolist(),input_array.flatten().var()])
    #calc std div[axis1, axis2, flattened],
    calculations['standard deviation']= calculations.get('standard deviation', [input_array.std(axis=0).tolist(),input_array.std(axis=1).tolist(),input_array.flatten().std()])
    #calc min [axis1, axis2, flattened]
    calculations['max']= calculations.get('max', [input_array.max(axis=0).tolist(),input_array.max(axis=1).tolist(),input_array.flatten().max()])

    #calc max [axis1, axis2, flattened]
    calculations['min']= calculations.get('min', [input_array.min(axis=0).tolist(),input_array.min(axis=1).tolist(),input_array.flatten().min()])
    #calc sum
    calculations['sum']= calculations.get('sum', [input_array.sum(axis=0).tolist(),input_array.sum(axis=1).tolist(),input_array.flatten().sum()])
    #return excpetion valueerror if len(inputlist) <> 9
  except ValueError:
    raise

  return calculations