input: 
  def linear_regression_model(input_dim: int, output_dim: int) -> tuple:
      """ Create a class in Python to model a linear regression function. The function
      should initialise the weights and bias to zero for the given dimensions, and
      then compute the forward pass for a given input.
      >>> weights, bias, out = linear_regression_model(2, 1)
      >>> np.all(weights == np.zeros((2, 1)))
      True
      >>> np.all(bias == np.zeros((1, 1)))
      True
      >>> out = linear_regression_model(2, 1)
      >>> np.all(out[0] == np.zeros((2, 1))) and np.all(out[1] == np.zeros((1, 1))) and np.all(out[2] == np.array([[0.], [0.]]))
      True
      """
      weights = np.zeros((input_dim, output_dim))
      bias = np.zeros((1, output_dim))
      x = np.ones((2, input_dim))
      out = np.dot(x, weights) + bias
      return weights, bias, out
  
output:     weights = np.zeros((input_dim, output_dim))
      bias = np.zeros((1, output_dim))
      x = np.ones((2, input_dim))
      out = np.dot(x, weights) + bias
      return weights, bias, out