input: Write a function to create a neural network class in Python to model a linear regression function. The class should initialize weights and bias for the linear regression model and implement a forward pass method to calculate the output. 
  
  assert LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3]]))==np.array([[0]])
  assert LinearRegressionModel(2, 2).forward(np.array([[1, 1], [2, 2]]))==np.array([[0, 0], [0, 0]])
  assert LinearRegressionModel(1, 3).forward(np.array([[1]]))==np.array([[0, 0, 0]])
  assert LinearRegressionModel(0, 0).forward(np.array([[]]))==np.array([[]])
  assert LinearRegressionModel(4, 2).forward(np.array([[1, 2, 3, 4]]))==np.array([[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