input: from typing import List
  
  def linear_regression_model(input_dim: int, output_dim: int) -> LinearRegressionModel:
      """Creates a neural network class in Python to model a linear regression function.
      The class initializes weights and bias and provides a method to compute the forward pass of the model.
      >>> model = linear_regression_model(2, 1)
      >>> model.forward(np.array([[1, 2]]))
      array([[0.]])
      >>> model = linear_regression_model(3, 2)
      >>> model.forward(np.array([[1, 2, 3]]))
      array([[0., 0.]])
      """
      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
  
      return LinearRegressionModel(input_dim, output_dim)
output:     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
  
      return LinearRegressionModel(input_dim, output_dim)