input: Write a function that can model a linear regression using a neural network class in Python. The class should initialize weights and bias, and have a method for performing the forward pass. The function should take input and output dimensions as parameters and instantiate the class.
  assert model_linear_regression(2, 1)==None
  assert model_linear_regression(3, 2)==None
  assert model_linear_regression(1, 3)==None
  assert model_linear_regression(0, 0)==None
  assert model_linear_regression(4, 4)==None
output: def model_linear_regression(input_dim, output_dim):
      class LinearRegression:
          def __init__(self, input_dim, output_dim):
              self.weights = np.random.randn(input_dim, output_dim)
              self.bias = np.random.randn(output_dim)
          
          def forward(self, x):
              return np.dot(x, self.weights) + self.bias
      
      if input_dim == 0 or output_dim == 0:
          return None
      
      model = LinearRegression(input_dim, output_dim)
      return None