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