Create a mini batch loop in VAE

I have an image dataset, which splits into 2500 patches. I use patches as VAE input and combine the Z latent space in the encoder.

Now I want to load 2500n as a batch. How can I use pyro.plate()in the guide and model to designate a 2500 mini-batch in a 2500n batch to finish VAE and combine the z in the minibatch?

ps. the input data are arranged in order

class VAE(nn.Module):
    # by default our latent space is 50-dimensional
    # and we use 400 hidden units
    def __init__(self, z_dim=16, hidden_dim=1000, use_cuda=True):
        # create the encoder and decoder networks
        self.encoder = Encoder(z_dim, hidden_dim)
        self.decoder = Decoder(z_dim, hidden_dim)
        if use_cuda:
            # calling cuda() here will put all the parameters of
            # the encoder and decoder networks into gpu memory
        self.use_cuda = use_cuda
        self.z_dim = z_dim
    # define the model p(x|z)p(z)
    def model(self, x):
        # register PyTorch module `decoder` with Pyro
        pyro.module("decoder", self.decoder)
        with pyro.plate("data", x.shape[0]):
            # setup hyperparameters for prior p(z)
            z_loc = x.new_zeros(torch.Size((x.shape[0], self.z_dim)))
            z_scale = x.new_ones(torch.Size((x.shape[0], self.z_dim)))
            # sample from prior (value will be sampled by guide when computing the ELBO)
            z = pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))
            # decode the latent code z
            loc_img = self.decoder(z)
            loc_img = loc_img.reshape(-1,200*200)
            pyro.sample("obs", dist.Bernoulli(loc_img).to_event(1), obs=x.reshape(-1, 200*200))

    # define the guide (i.e. variational distribution) q(z|x)
    def guide(self, x):
        # register PyTorch module `encoder` with Pyro
        pyro.module("encoder", self.encoder)
        with pyro.plate("data", x.shape[0]):
            # use the encoder to get the parameters used to define q(z|x)
            z_loc, z_scale = self.encoder(x)
            # sample the latent code z
            pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))