input: Write a function to implement a neural network class in Python to model a linear regression function. The class should initialize the weights and bias for the linear regression model, and provide a method to calculate the forward pass.
  
  class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          ''' Initialises the weights and bias of the linear regression model
          Arguments:
              input_dim {int} -- Number of input features
              output_dim {int} -- Number of outputs
          '''
          self.weights = np.zeros((input_dim, output_dim))
          self.bias = np.zeros((1, output_dim))
  
      def forward(self, x):
          ''' Calculates the forward pass of the linear regression model
          Arguments:
              x {numpy array} -- Input data of size (N, input_dim)
          Returns:
              out {numpy array} -- Output of the linear regression model of size (N, output_dim)
          '''
          out = np.dot(x, self.weights) + self.bias
          return out
  
  assert isinstance(LinearRegressionModel(3, 1), object)==True
  assert LinearRegressionModel(3, 1).weights.shape==(3, 1)
  assert LinearRegressionModel(3, 1).bias.shape==(1, 1)
  assert np.array_equal(LinearRegressionModel(2, 1).forward(np.array([[1, 2]])), [[0]])==True
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):
          out = np.dot(x, self.weights) + self.bias
          return out
  