# Cannot pass a variable to CustomDist that has more dimensions than CustomDist's dimension

**URL:** <https://discourse.pymc.io/t/cannot-pass-a-variable-to-customdist-that-has-more-dimensions-than-customdists-dimension/11894>\
**Category:** v5\
**Tags:** bug\
**Created:** [April 14, 2023, 12:46am UTC](https://discourse.pymc.io/t/cannot-pass-a-variable-to-customdist-that-has-more-dimensions-than-customdists-dimension/11894 "2023-04-14T00:46:16Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Paulo\_Soares](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/paulo_soares/32/5987_2.png) [@Paulo\_Soares](https://discourse.pymc.io/u/Paulo_Soares)\
**Post date:** [April 14, 2023, 12:46am UTC](https://discourse.pymc.io/t/cannot-pass-a-variable-to-customdist-that-has-more-dimensions-than-customdists-dimension/11894/1 "2023-04-14T00:46:16Z")

</div>

As the title says. Below is a minimal example. This code breaks with the error below even if `dummy` is never used in `logp_fn`. The code works if we remove `dummy` from the parameters and function signature, or if we remove the second dimension in the size of `dummy`, i.e., (5,) instead of (5,5).

Error:

```auto
ValueError: Size length is incompatible with batched dimensions of parameter 2 dummy:
len(size) = 1, len(batched dims dummy) = 2. Size length must be 0 or >= 2

```

Code:

```auto
from typing import Any

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc as pm
import pytensor.tensor as ptt

def logp_fn(x: Any,
            mu: Any,
            sigma: Any,
            dummy: Any):
    return pm.logp(pm.Normal.dist(mu=mu, sigma=sigma), x).sum()

if __name__ == " __main__":
    evidence = np.random.normal(loc=3, size=1000)

    with pm.Model() as model:
        logp_params = [
            ptt.ones(1) * 3,
            ptt.ones(1),
            pm.Normal("dummy", mu=0, sigma=1, size=(5,5))
        ]
        latent = pm.DensityDist("custom", *logp_params, logp=logp_fn, size=(1,))

        obs = pm.Normal("observation", mu=latent, sigma=1, observed=evidence)

        idata = pm.sample(1000, init="jitter+adapt_diag", tune=1000, chains=2, random_seed=0, cores=2)
        az.plot_trace(idata)
        plt.show()

```