# Buggy code
def get_likelihood_parameters(
    self,
    adata: Optional[AnnData] = None,
    indices: Optional[Sequence[int]] = None,
    n_samples: Optional[int] = 1,
    give_mean: Optional[bool] = False,
    batch_size: Optional[int] = None,
) -> Dict[str, np.ndarray]:
    r"""
    Estimates for the parameters of the likelihood :math:`p(x \mid z)`

    Parameters
    ----------
    adata
        AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
        AnnData object used to initialize the model.
    indices
        Indices of cells in adata to use. If `None`, all cells are used.
    n_samples
        Number of posterior samples to use for estimation.
    give_mean
        Return expected value of parameters or a samples
    batch_size
        Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`.
    """
    adata = self._validate_anndata(adata)
    scdl = self._make_data_loader(adata=adata, indices=indices, batch_size=batch_size)

    dropout_list = []
    mean_list = []
    dispersion_list = []
    for tensors in scdl:
        inference_kwargs = dict(n_samples=n_samples)
        _, generative_outputs = self.module.forward(
            tensors=tensors,
            inference_kwargs=inference_kwargs,
            compute_loss=False,
        )
        px_r = generative_outputs["px_r"]
        px_rate = generative_outputs["px_rate"]
        px_dropout = generative_outputs["px_dropout"]

        n_batch = px_rate.size(0) if n_samples == 1 else px_rate.size(1)
        dispersion_list += [
            np.repeat(np.array(px_r.cpu())[np.newaxis, :], n_batch, axis=0)
        ]
        mean_list += [np.array(px_rate.cpu())]
        dropout_list += [np.array(px_dropout.cpu())]

    dropout = np.concatenate(dropout_list)
    means = np.concatenate(mean_list)
    dispersions = np.concatenate(dispersion_list)
    if give_mean and n_samples > 1:
        dropout = dropout.mean(0)
        means = means.mean(0)

    return_dict = {}
    return_dict["mean"] = means

    if self.module.gene_likelihood == "zinb":
        return_dict["dropout"] = dropout
        return_dict["dispersions"] = dispersions
    if self.module.gene_likelihood == "nb":
        return_dict["dispersions"] = dispersions

    return return_dict


# Traceback (ValueError)
509         dropout = np.concatenate(dropout_list)
510         means = np.concatenate(mean_list)
--> 511         dispersions = np.concatenate(dispersion_list)
512         if give_mean and n_samples > 1:
513             dropout = dropout.mean(0)

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 128 and the array at index 24 has size 51```

# Fixed code
def get_likelihood_parameters(
    self,
    adata: Optional[AnnData] = None,
    indices: Optional[Sequence[int]] = None,
    n_samples: Optional[int] = 1,
    give_mean: Optional[bool] = False,
    batch_size: Optional[int] = None,
) -> Dict[str, np.ndarray]:
    r"""
    Estimates for the parameters of the likelihood :math:`p(x \mid z)`

    Parameters
    ----------
    adata
        AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
        AnnData object used to initialize the model.
    indices
        Indices of cells in adata to use. If `None`, all cells are used.
    n_samples
        Number of posterior samples to use for estimation.
    give_mean
        Return expected value of parameters or a samples
    batch_size
        Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`.
    """
    adata = self._validate_anndata(adata)
    scdl = self._make_data_loader(adata=adata, indices=indices, batch_size=batch_size)

    dropout_list = []
    mean_list = []
    dispersion_list = []
    for tensors in scdl:
        inference_kwargs = dict(n_samples=n_samples)
        _, generative_outputs = self.module.forward(
            tensors=tensors,
            inference_kwargs=inference_kwargs,
            compute_loss=False,
        )
        px_r = generative_outputs["px_r"]
        px_rate = generative_outputs["px_rate"]
        px_dropout = generative_outputs["px_dropout"]

        n_batch = px_rate.size(0) if n_samples == 1 else px_rate.size(1)

        px_r = np.array(px_r.cpu())
        if len(px_r.shape) == 1:
            dispersion_list += [np.repeat(px_r[np.newaxis, :], n_batch, axis=0)]
        else:
            dispersion_list += [px_r]
        mean_list += [np.array(px_rate.cpu())]
        dropout_list += [np.array(px_dropout.cpu())]

    dropout = np.concatenate(dropout_list)
    means = np.concatenate(mean_list)
    dispersions = np.concatenate(dispersion_list)
    if give_mean and n_samples > 1:
        dropout = dropout.mean(0)
        means = means.mean(0)

    return_dict = {}
    return_dict["mean"] = means

    if self.module.gene_likelihood == "zinb":
        return_dict["dropout"] = dropout
        return_dict["dispersions"] = dispersions
    if self.module.gene_likelihood == "nb":
        return_dict["dispersions"] = dispersions

    return return_dict