human
Solve the following python programming problem given the following code:
# First we need some boilerplate to create a class and define a .model() method.
class Model1(ForecastingModel):
    # We then implement the .model() method. Since this is a generative model, it shouldn't
    # look at data.
however it is convenient to see the shape of data we're supposed to
    # generate, so this inputs a zeros_like(data) tensor instead of the actual data.
    def model(self, zero_data, covariates):
        data_dim = zero_data.size(-1)  # Should be 1 in this univariate tutorial.
        feature_dim = covariates.size(-1)

        # The first part of the model is a probabilistic program to create a prediction.
        # We use the zero_data as a template for the shape of the prediction.
        bias = pyro.sample("bias", dist.Normal(0, 10).expand([data_dim]).to_event(1))
        weight = pyro.sample("weight", dist.Normal(0, 0.1).expand([feature_dim]).to_event(1))
        prediction = bias + (weight * covariates).sum(-1, keepdim=True)
        # The prediction should have the same shape as zero_data (duration, obs_dim),
        # but may have additional sample dimensions on the left.
        assert prediction.shape[-2:] == zero_data.shape

        # The next part of the model creates a likelihood or noise distribution.
        # Again we'll be Bayesian and write this as a probabilistic program with
        # priors over parameters.
        noise_scale = pyro.sample("noise_scale", dist.LogNormal(-5, 5).expand([1]).to_event(1))
        noise_dist = dist.Normal(0, noise_scale)

        # The final step is to call the .predict() method.
        self.predict(noise_dist, prediction)
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Let's start with a simple log-linear regression model, with no trend or seasonality. Note that while this example is univariate, Pyro's forecasting framework is multivariate, so we'll often need to reshape using .unsqueeze(-1), .expand([1]), and .to_event(1).