input: Write a function to create a class in Python that models a linear regression function using a neural network approach. The class should include an initialization method to set the weights and bias, and a forward method to compute the output given input data. The initialization method should accept the dimensions of the input features and the output as arguments. The forward method should accept input data as a numpy array and return the output of the linear regression model. If the input data is empty, the function should return an empty numpy array.
  
  from LinearRegressionModel import LinearRegressionModel
  import numpy as np
  
  # Test cases
  model = LinearRegressionModel(input_dim=3, output_dim=1)
  assert np.array_equal(model.__init__(3, 1).weights, np.zeros((3, 1))) == True
  assert np.array_equal(model.__init__(3, 1).bias, np.zeros((1, 1))) == True
  assert np.array_equal(model.forward(np.array([[1, 2, 3]])), [[0.]]) == True
  assert np.array_equal(model.forward(np.array([])), np.array([])) == True
output: 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 x.size == 0:
          return np.array([])
      return np.dot(x, self.weights) + self.bias