input: Write a function to create a neural network class in Python that models a linear regression function. The class should have an initializer to set up the weights and bias, and a forward method to calculate the output based on input data. The function should be named `create_linear_regression_model`.
  
  assert create_linear_regression_model(3, 1) == {
      "weights": np.zeros((3, 1)),
      "bias": np.zeros((1, 1))
  }
  
  assert isinstance(create_linear_regression_model(2, 2).forward(np.array([[1, 2], [3, 4]])), np.ndarray)
output: def create_linear_regression_model(input_dim, output_dim):
      class LinearRegressionModel:
          def __init__(self, input_dim, output_dim):
              self.weights = np.zeros((input_dim, output_dim))
              self.bias = np.zeros((1, output_dim))
          
          def forward(self, x):
              return np.dot(x, self.weights) + self.bias
      
      return LinearRegressionModel(input_dim, output_dim)