human
Solve the following python programming problem given the following code:
# Create the neural network. This takes a latent code, z_what, to pixel intensities.
class Decoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Linear(50, 200)
        self.l2 = nn.Linear(200, 400)

    def forward(self, z_what):
        h = relu(self.l1(z_what))
        return sigmoid(self.l2(h))

decode = Decoder()

z_where_prior_loc = torch.tensor([3., 0., 0.])
z_where_prior_scale = torch.tensor([0.1, 1., 1.])
z_what_prior_loc = torch.zeros(50)
z_what_prior_scale = torch.ones(50)

def prior_step_sketch(t):
    # Sample object pose. This is a 3-dimensional vector representing x,y position and size.
    z_where = pyro.sample('z_where_{}'.format(t),
                          dist.Normal(z_where_prior_loc.expand(1, -1),
                                      z_where_prior_scale.expand(1, -1))
                              .to_event(1))

    # Sample object code. This is a 50-dimensional vector.
    z_what = pyro.sample('z_what_{}'.format(t),
                         dist.Normal(z_what_prior_loc.expand(1, -1),
                                     z_what_prior_scale.expand(1, -1))
                             .to_event(1))
    
    # Map code to pixel space using the neural network.
    y_att = decode(z_what)

    # Position/scale object within larger image.
    y = object_to_image(z_where, y_att)

    return y
Given the above code, generate some markdown instructions for it.
assistant
#Here, to pixel intensities.

To get an idea where we're heading, we first give a brief overview of the model and the approach we'll take to inference. We'll follow the naming conventions used in [1] as closely as possible.
AIR decomposes the process of generating an image into discrete steps, each of which generates only part of the image. More specifically, at each step the model will generate a small image (y_att) by passing a latent "code" variable (z_what) through a neural network. We'll refer to these small images as "objects". In the case of AIR applied to the multi-mnist dataset we expect each of these objects to represent a single digit. The model also includes uncertainty about the location and size of each object. We'll describe an object's location and size as its "pose" (z_where). To produce the final image, each object will first be located within a larger image (y) using the pose infomation z_where. Finally, the ys from all time steps will be combined additively to produce the final image x.
Here's a picture (reproduced from [1]) that shows two steps of this process:
Inference is performed in this model using amortized stochastic variational inference (SVI). The parameters of the neural network are also optimized during inference. Performing inference in such rich models is always difficult, but the presence of discrete choices (the number of steps in this case) makes inference in this model particularly tricky. For this reason the authors use a technique called data dependent baselines to achieve good performance. This technique can be implemented in Pyro, and we'll see how later in the tutorial.
Model
Generating a single object
Let's look at the model more closely. At the core of the model is the generative process for a single object. Recall that:

At each step a single object is generated.
Each object is generated by passing its latent code through a neural network.
We maintain uncertainty about the latent code used to generate each object, as well as its pose.

This can be expressed in Pyro like so: