more fix

docs/qml/index.md

@@ -30,7 +30,7 @@ fm = qd.kron(RX(i, acos(fp)) for i in range(n_qubits))
 # the name of the assigned to the feature parameter
 inputs = {"phi": torch.rand(3)}
 samples = qd.sample(fm, values=inputs)
-print(samples[0])  # markdown-exec: hide
+print(f"samples = {samples[0]}")  # markdown-exec: hide
 ```
 
 The [`constructors.feature_map`][qadence.constructors.feature_map] module provides

docs/qml/ml_tools.md

@@ -50,7 +50,7 @@ for i in range(n_epochs):
 ## Optimization routines
 
 For training QML models, Qadence also offers a few out-of-the-box routines for optimizing differentiable
-models, _e.g._ `QNN`s and `QuantumModel`s containing either *trainable* and/or *non-trainable* parameters
+models, _e.g._ `QNN`s and `QuantumModel`, containing either *trainable* and/or *non-trainable* parameters
 (see [the parameters tutorial](../tutorials/parameters.md) for detailed information about parameter types):
 
 * [`train_with_grad`][qadence.ml_tools.train_with_grad] for gradient-based optimization using PyTorch native optimizers