input: Write a function to implement a linear regression model using a neural network class in Python. The function should create a class named `LinearRegressionModel` that initializes weights and bias, and performs a forward pass to predict outputs.
  assert LinearRegressionModel(input_dim=3, output_dim=1).forward(np.array([[1, 2, 3]])) == np.array([[0]])
  assert LinearRegressionModel(input_dim=2, output_dim=2).forward(np.array([[4, 5]])) == np.array([[0, 0]])
  assert LinearRegressionModel(input_dim=1, output_dim=4).forward(np.array([[6]])) == np.array([[0, 0, 0, 0]])
  assert LinearRegressionModel(input_dim=5, output_dim=2).forward(np.array([[7, 8, 9, 10, 11]])) == np.array([[0, 0]])
  assert LinearRegressionModel(input_dim=2, output_dim=3).forward(np.array([[12, 13]])) == np.array([[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