input: def LinearRegressionModel(input_dim: int, output_dim: int):
      """ Creates a neural network class in Python to model a linear regression function.
      The class initialises the weights and bias of the linear regression model and calculates 
      the forward pass of the linear regression model.
      Example:
      >>> model = LinearRegressionModel(2, 1)
      >>> model.weights
      array([[0.], [0.]])
      >>> model.bias
      array([[0.]])
      >>> model.forward(np.array([[1, 2], [3, 4]]))
      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, x: np.ndarray) -> np.ndarray:
              return np.dot(x, self.weights) + self.bias
  