input: 
  def LinearRegressionModel(input_dim: int, output_dim: int) -> None:
      """ Create a neural network class in Python to model a linear regression function.
      The class should initialize the weights and bias of the model, and it should include a 
      forward method to calculate the forward pass of the model.
      >>> model = LinearRegressionModel(2, 1)
      >>> model.weights
      array([[0.],
             [0.]])
      >>> model.bias
      array([[0.]])
      >>> model.forward(np.array([[1, 2], [3, 4]]))
      array([[0.],
             [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:     return LinearRegressionModel(input_dim, output_dim)