STYLE: Use the GradientTable class for type hinting

Use the `dipy.core.gradientsGradientTable` class for type hinting
instead of `dipy.core.gradients.gradient_table`: the latter is a
function name and hence does not provide type information.

Fixes:
```
src/nifreeze/testing/simulations.py:151: error:
 Function "dipy.core.gradients.gradient_table" is not valid as a type  [valid-type]
src/nifreeze/testing/simulations.py:151: note:
 Perhaps you need "Callable[...]" or a callback protocol?
src/nifreeze/testing/simulations.py:185: error:
 Function "dipy.core.gradients.gradient_table" is not valid as a type  [valid-type]
src/nifreeze/testing/simulations.py:185: note:
 Perhaps you need "Callable[...]" or a callback protocol?
src/nifreeze/testing/simulations.py:207: error:
 gradient_table? has no attribute "b0s_mask"  [attr-defined]
src/nifreeze/testing/simulations.py:208: error:
 gradient_table? has no attribute "bvecs"  [attr-defined]
```

Author: jhlegarreta

src/nifreeze/testing/simulations.py

@@ -27,7 +27,7 @@
 import nibabel as nb
 import numpy as np
 from dipy.core.geometry import sphere2cart
-from dipy.core.gradients import gradient_table
+from dipy.core.gradients import GradientTable, gradient_table
 from dipy.core.sphere import HemiSphere, Sphere, disperse_charges
 from dipy.sims.voxel import all_tensor_evecs, multi_tensor, single_tensor
 
@@ -148,7 +148,7 @@ def create_diffusion_encoding_gradient_dirs(
 
 def create_single_shell_gradient_table(
     hsph_dirs: int, bval_shell: float, iterations: int = 5000
-) -> gradient_table:
+) -> GradientTable:
     """
     Create a single-shell gradient table.
 
@@ -163,7 +163,7 @@ def create_single_shell_gradient_table(
 
     Returns
     -------
-    :obj:`~dipy.core.gradients.gradient_table`
+    :obj:`~dipy.core.gradients.GradientTable`
         The gradient table for the single-shell.
 
     """
@@ -182,7 +182,7 @@ def create_single_shell_gradient_table(
 
 
 def get_query_vectors(
-    gtab: gradient_table, train_mask: np.ndarray
+    gtab: GradientTable, train_mask: np.ndarray
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Get the diffusion-encoding gradient vectors for estimation from the gradient table.
@@ -193,7 +193,7 @@ def get_query_vectors(
 
     Parameters
     ----------
-    gtab : :obj:`~dipy.core.gradients.gradient_table`
+    gtab : :obj:`~dipy.core.gradients.GradientTable`
         Gradient table.
     train_mask : :obj:`~numpy.ndarray`
         Mask for selecting training vectors.