input: Write a function to model a linear regression using a neural network class in Python with specified input and output dimensions. The class should initialize the weights and bias, and define a forward method to compute the output from input data. If the input dimension or output dimension is not provided, raise a ValueError.
  assert LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3]])) == array([[0.]])
  assert LinearRegressionModel(2, 2).forward(np.array([[1, 2], [3, 4]])) == array([[0., 0.], [0., 0.]])
  try:
      LinearRegressionModel(None, 1)
      raise AssertionError("ValueError not raised")
  except ValueError:
      pass
  assert LinearRegressionModel(1, None)
  try:
      LinearRegressionModel(0, 1)
      raise AssertionError("ValueError not raised")
  except ValueError:
      pass
output: class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          if input_dim is None or output_dim is None or input_dim <= 0:
              raise ValueError("Input and output dimensions must be positive integers.")
          self.weights = np.zeros((input_dim, output_dim))
          self.bias = np.zeros(output_dim)
  
      def forward(self, x):
          return np.dot(x, self.weights) + self.bias