input: Write a function to create a neural network class in Python that models a linear regression function. The class should include methods to initialize the weights and bias, and a forward pass method to compute the output given input data. The input data should be a numpy array of shape (N, input_dim), and the output should be a numpy array of shape (N, output_dim).
  
  
  import numpy as np
  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
  
  # Test cases
  model = LinearRegressionModel(3, 1)
  assert model.forward(np.array([[1, 2, 3]])) == array([[0.]])
  
  model = LinearRegressionModel(2, 2)
  assert np.array_equal(model.forward(np.array([[1, 2], [3, 4]])), np.array([[0, 0], [0, 0]]))
  
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):
      out = np.dot(x, self.weights) + self.bias
      return out
  