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, and include a method to perform the forward pass computation. If the input is empty, return an empty list.
  
  assert LinearRegressionModel(input_dim=3, output_dim=1).weights.shape == (3, 1)
  assert LinearRegressionModel(input_dim=4, output_dim=2).bias.shape == (1, 2)
  assert np.array_equal(LinearRegressionModel(input_dim=2, output_dim=1).forward(np.array([[1, 2]])), np.array([[0]]))
  assert np.array_equal(LinearRegressionModel(input_dim=2, output_dim=1).forward(np.array([[1, 3]])), np.array([[0]]))
  assert np.array_equal(LinearRegressionModel(input_dim=3, output_dim=1).forward([]), [])
output: 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):
          if len(x) == 0:
              return []
          return np.dot(x, self.weights) + self.bias