input: Write a function to create a neural network class in Python that models a linear regression function. The class should include an initialization method to set the weights and bias, and a forward method to calculate the forward pass. The neural network should take input features and output predictions.
  assert LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3]])) == np.array([[0]])
  assert LinearRegressionModel(2, 2).forward(np.array([[4, 5]])) == np.array([[0, 0]])
  assert LinearRegressionModel(1, 3).forward(np.array([[6]])) == np.array([[0, 0, 0]])
output: class LinearRegressionModel:
      def __init__(self, input_size, output_size):
          self.weights = np.zeros((input_size, output_size))
          self.bias = np.zeros(output_size)
  
      def forward(self, x):
          return np.dot(x, self.weights) + self.bias