mini fixes in the doc (#71)

NVIDIA · Jan 21, 2025 · 25e588e · 25e588e
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diff --git a/docs/tutorials/etp.rst b/docs/tutorials/etp.rst
@@ -16,7 +16,7 @@
 Equivariant Tensor Product
 ==========================
 
-The submodule :class:`cuequivariance.descriptors` contains many descriptors of Equivariant Tensor Products (:class:`cuequivariance.EquivariantTensorProduct`).
+The submodule :class:`cuequivariance.descriptors` contains many descriptors of Equivariant Tensor Products represented by the class :class:`cuequivariance.EquivariantTensorProduct`.
 
 Examples
 --------
@@ -59,6 +59,7 @@ Execution on JAX
 
     import jax
     import jax.numpy as jnp
+    import cuequivariance as cue
     import cuequivariance_jax as cuex
 
     e = cue.descriptors.linear(
@@ -83,6 +84,7 @@ We can execute an :class:`cuequivariance.EquivariantTensorProduct` with PyTorch.
 .. jupyter-execute::
 
     import torch
+    import cuequivariance as cue
     import cuequivariance_torch as cuet
 
     e = cue.descriptors.linear(

diff --git a/docs/tutorials/stp.rst b/docs/tutorials/stp.rst
@@ -50,7 +50,7 @@ The subscripts of this tensor product are "uv,iu,iv" where "uv" represents the m
    d
 
 Each operand of the tensor product descriptor has a list of segments.
-We can add segments to the descriptor using the `add_segment` method.
+We can add segments to the descriptor using the :meth:`add_segment <cuequivariance.SegmentedTensorProduct.add_segment>` method.
 We can add the segments of the input and output representations to the descriptor.
 
 .. jupyter-execute::
@@ -90,15 +90,15 @@ Finally, we can normalize the paths for the last operand such that the output is
 
 As we can see, the paths coefficients has been normalized.
 
-Now we can create a tensor product from the descriptor and execute it. In PyTorch, we can use the :class:`cuet.TensorProduct` class.
+Now we can create a tensor product from the descriptor and execute it. In PyTorch, we can use the :class:`cuet.TensorProduct <cuequivariance_torch.TensorProduct>` class.
 
 .. jupyter-execute::
 
    linear_torch = cuet.TensorProduct(d, use_fallback=True)
    linear_torch
 
 
-In JAX, we can use the :func:`cuex.tensor_product` function.
+In JAX, we can use the :func:`cuex.tensor_product <cuequivariance_jax.tensor_product>` function.
 
 .. jupyter-execute::