input: Write a function to create a neural network class in Python to model a linear regression function. The class should initialize the weights and bias of the linear regression model and perform the forward pass for prediction. If the input data is empty, the function should return None.
  assert LinearRegressionModel(2, 1) is not None
  assert LinearRegressionModel(0, 0) is not None
  assert LinearRegressionModel(0, 1) is None
  assert LinearRegressionModel(3, 2).forward(np.array([[1, 2, 3]])) is not None
  assert LinearRegressionModel(4, 0).forward(np.array([[1, 2, 3, 4]])) is None
output: class LinearRegressionModel:
      def __init__(self, n_features, n_targets):
          if n_features == 0:
              self.valid = False
              return
          self.valid = True
          self.weights = np.zeros((n_features, n_targets))
          self.bias = np.zeros(n_targets)
  
      def forward(self, x):
          if not self.valid or x.size == 0:
              return None
          return np.dot(x, self.weights) + self.bias