input: Write a function to filter out all negative numbers from a given matrix. If a number in the matrix is negative, replace it with 0. If the matrix is empty, return an empty matrix.
  def filter_out_negative(matrix):
    if not matrix:
      return []
    # Convert matrix to numpy array
    matrix = np.array(matrix)
    # Find all negative numbers and set them to 0
    matrix[matrix < 0] = 0
    return matrix
  
  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]]))==True
  assert np.array_equal(filter_out_negative([[1, -2, 3], [-4, 5, 6], [7, 8, -9]]), np.array([[1, 0, 3], [0, 5, 6], [7, 8, 0]]))==True
  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]]))==True
  assert np.array_equal(filter_out_negative([]), [])==True
output: 
  def filter_out_negative(matrix):
    if not matrix:
      return []
    return [[0 if x < 0 else x for x in row] for row in matrix]
  