input: 
  def NeuralNetworkModel(input_dim, output_dim):
      """ Creates a neural network class to model a linear regression function.
      The class should initialize the weights and bias, and perform the forward pass.
      Example:
      model = NeuralNetworkModel(2, 1)
      print(model.forward(np.array([[1, 2]])))  # Should return [[0]]
      """
  
  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
  
output:     
  def NeuralNetworkModel(input_dim, output_dim):
      """ Creates a neural network class to model a linear regression function.
      The class should initialize the weights and bias, and perform the forward pass.
      Example:
      model = NeuralNetworkModel(2, 1)
      print(model.forward(np.array([[1, 2]])))  # Should return [[0]]
      """
  
  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
  