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, and have a forward pass method
      to compute the output given some input data.
      Example:
      lr_model = LinearRegressionModel(2, 1)
      output = lr_model.forward(np.array([[1, 2]]))
      assert output.shape == (1, 1)
      assert np.all(output == [[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
  