input: def LinearRegressionModel(input_dim: int, output_dim: int) -> None:
      """
      Creates a neural network class in Python to model a linear regression function.
      The class initializes the weights and bias of the linear regression model and
      provides a method to calculate the forward pass.
      
      Example:
      >>> model = LinearRegressionModel(2, 1)
      >>> model.weights
      array([[0.],
             [0.]])
      >>> model.bias
      array([[0.]])
      >>> input_data = np.array([[1, 2], [3, 4]])
      >>> model.forward(input_data)
      array([[0.],
             [0.]])
      """
output:     
      import numpy as np
  
      class LinearRegressionModel:
          def __init__(self, input_dim: int, output_dim: int):
              self.weights = np.zeros((input_dim, output_dim))
              self.bias = np.zeros((1, output_dim))
  
          def forward(self, input_data: np.ndarray) -> np.ndarray:
              return np.dot(input_data, self.weights) + self.bias
  