input: Write a function to create a neural network class in Python to model a linear regression function. Include an initialization method to set the weights and bias, and a forward method to compute the output of the model.
  assert isinstance(LinearRegressionModel(3, 1), LinearRegressionModel) == True
  assert np.array_equal(LinearRegressionModel(3, 1).weights, np.zeros((3, 1))) == True
  assert np.array_equal(LinearRegressionModel(3, 1).bias, np.zeros((1, 1))) == True
  assert np.array_equal(LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3], [4, 5, 6]])), np.array([[0], [0]])) == True
output: class LinearRegressionModel:
      def __init__(self, input_size, output_size):
          self.weights = np.zeros((input_size, output_size))
          self.bias = np.zeros((1, output_size))
  
      def forward(self, x):
          return np.dot(x, self.weights) + self.bias