input: from typing import List
  
  def create_neural_network_class(input_dim: int, output_dim: int) -> str:
      """Creates a Python class to model a linear regression neural network.
      The class should initialize weights and bias, and have a method to perform the forward pass.
      >>> create_neural_network_class(3, 1)
      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:     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