input: Write a function to filter out all negative numbers from the given matrix. If the matrix contains any negative numbers, replace them with 0. If the matrix is empty, return an empty matrix. The matrix should be returned as a NumPy array.
  assert np.array_equal(filter_out_negative([[-1, 2, -3], [-4, 5, 6], [-7, 8, 9]]), np.array([[0, 2, 0], [0, 5, 6], [0, 8, 9]]))
  assert np.array_equal(filter_out_negative([[]]), np.array([[]]))
  assert np.array_equal(filter_out_negative([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
  assert np.array_equal(filter_out_negative([[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]), np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]))
output: 
  import numpy as np
  
  def filter_out_negative(matrix):
      if len(matrix) == 0:
          return np.array([])
      return np.where(np.array(matrix) < 0, 0, np.array(matrix))
  