fix tutorials

docs/how_to_guide/plot_06_sklearn_pipeline_cv_demo.md

@@ -321,7 +321,8 @@ scores = np.zeros((len(regularizer_strength) * len(n_basis_funcs), n_folds))
 coeffs = {}
 
 # initialize basis and model
-basis = nmo.basis.TransformerBasis(nmo.basis.RaisedCosineLinearEval(6))
+basis = nmo.basis.RaisedCosineLinearEval(6).set_input_shape(1)
+basis = nmo.basis.TransformerBasis(basis)
 model = nmo.glm.GLM(regularizer="Ridge")
 
 # loop over combinations
@@ -441,13 +442,13 @@ We are now able to capture the distribution of the firing rate appropriately: bo
 
 In the previous example we set the number of basis functions of the [`Basis`](nemos.basis._basis.Basis) wrapped in our [`TransformerBasis`](nemos.basis._transformer_basis.TransformerBasis). However, if we are for example not sure about the type of basis functions we want to use, or we have already defined some basis functions of our own, then we can use cross-validation to directly evaluate those as well.
 
-Here we include `transformerbasis___basis` in the parameter grid to try different values for `TransformerBasis._basis`:
+Here we include `transformerbasis__basis` in the parameter grid to try different values for `TransformerBasis.basis`:
 
 
 ```{code-cell} ipython3
 param_grid = dict(
     glm__regularizer_strength=(0.1, 0.01, 0.001, 1e-6),
-    transformerbasis___basis=(
+    transformerbasis__basis=(
         nmo.basis.RaisedCosineLinearEval(5).set_input_shape(1),
         nmo.basis.RaisedCosineLinearEval(10).set_input_shape(1),
         nmo.basis.RaisedCosineLogEval(5).set_input_shape(1),
@@ -481,7 +482,7 @@ cvdf = pd.DataFrame(gridsearch.cv_results_)
 # Read out the number of basis functions
 cvdf["transformerbasis_config"] = [
     f"{b.__class__.__name__} - {b.n_basis_funcs}"
-    for b in cvdf["param_transformerbasis___basis"]
+    for b in cvdf["param_transformerbasis__basis"]
 ]
 
 cvdf_wide = cvdf.pivot(
@@ -537,7 +538,7 @@ Please note that because it would lead to unexpected behavior, mixing the two wa
 param_grid = dict(
     glm__regularizer_strength=(0.1, 0.01, 0.001, 1e-6),
     transformerbasis__n_basis_funcs=(3, 5, 10, 20, 100),
-    transformerbasis___basis=(
+    transformerbasis__basis=(
         nmo.basis.RaisedCosineLinearEval(5).set_input_shape(1),
         nmo.basis.RaisedCosineLinearEval(10).set_input_shape(1),
         nmo.basis.RaisedCosineLogEval(5).set_input_shape(1),
@@ -592,7 +593,7 @@ cvdf = pd.DataFrame(gridsearch.cv_results_)
 # Read out the number of basis functions
 cvdf["transformerbasis_config"] = [
     f"{b.__class__.__name__} - {b.n_basis_funcs}"
-    for b in cvdf["param_transformerbasis___basis"]
+    for b in cvdf["param_transformerbasis__basis"]
 ]
 
 cvdf_wide = cvdf.pivot(