Add tensor_like to glossary (#5498)

* Add tensor_like to glossary

Inspired by numpy array_like

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Add Model class to numpydoc aliases

Co-authored-by: Oriol Abril-Pla <[email protected]>

Co-authored-by: Oriol Abril-Pla <[email protected]>

Author: ricardoV94

docs/source/conf.py

@@ -49,6 +49,7 @@
     "notfound.extension",
     "sphinx_copybutton",
     "sphinx_remove_toctrees",
+    "jupyter_sphinx",
 ]
 
 # Don't auto-generate summary for class members.
@@ -74,8 +75,10 @@
     "MultiTrace": ":class:`~pymc.backends.base.MultiTrace`",
     "BaseTrace": ":class:`~pymc.backends.base.BaseTrace`",
     "Point": ":class:`~pymc.Point`",
+    "Model": ":class:`~pymc.Model`",
     "SMC_kernel": ":ref:`SMC Kernel <smc_kernels>`",
     "Aesara_Op": ":class:`Aesara Op <aesara.graph.op.Op>`",
+    "tensor_like": ":term:`tensor_like`",
 }
 
 # Show the documentation of __init__ and the class docstring

docs/source/glossary.md

@@ -124,4 +124,13 @@ Hierarchical Ordinary Differential Equation
 [MCMC](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)
   Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a {term}`Markov Chain` that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain.  Various algorithms exist for constructing chains, including the Metropolis–Hastings algorithm.
 
+tensor_like
+  Any scalar or sequence that can be interpreted as a {class}`~aesara.tensor.TensorVariable`. In addition to TensorVariables, this includes NumPy ndarrays, scalars, lists and tuples (possibly nested). Any argument accepted by `aesara.tensor.as_tensor_variable` is tensor_like.
+
+  ```{jupyter-execute}
+  import aesara.tensor as at
+
+  at.as_tensor_variable([[1, 2.0], [0, 0]])
+  ```
+
 :::::