📝 [array] Add examples for EnsureType and CastToType

monai/transforms/utility/array.py

@@ -333,6 +333,23 @@ class CastToType(Transform):
     """
     Cast the Numpy data to specified numpy data type, or cast the PyTorch Tensor to
     specified PyTorch data type.
+
+    Example:
+        >>> import numpy as np
+        >>> import torch
+        >>> transform = CastToType(dtype=np.float32)
+
+        >>> # Example with a numpy array
+        >>> img_np = np.array([0, 127, 255], dtype=np.uint8)
+        >>> img_np_casted = transform(img_np)
+        >>> img_np_casted
+        array([  0. , 127. , 255. ], dtype=float32)
+
+        >>> # Example with a PyTorch tensor
+        >>> img_tensor = torch.tensor([0, 127, 255], dtype=torch.uint8)
+        >>> img_tensor_casted = transform(img_tensor)
+        >>> img_tensor_casted
+        tensor([  0., 127., 255.])  # dtype is float32
     """
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
@@ -413,10 +430,26 @@ class EnsureType(Transform):
         dtype: target data content type to convert, for example: np.float32, torch.float, etc.
         device: for Tensor data type, specify the target device.
         wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
-            E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.
         track_meta: if `True` convert to ``MetaTensor``, otherwise to Pytorch ``Tensor``,
             if ``None`` behave according to return value of py:func:`monai.data.meta_obj.get_track_meta`.
 
+    Example with wrap_sequence=True:
+        >>> import numpy as np
+        >>> import torch
+        >>> transform = EnsureType(data_type="tensor", wrap_sequence=True)
+        >>> # Converting a list to a tensor
+        >>> data_list = [1, 2., 3]
+        >>> tensor_data = transform(data_list)
+        >>> tensor_data
+        tensor([1., 2., 3.])    # All elements have dtype float32
+
+    Example with wrap_sequence=False:
+        >>> transform = EnsureType(data_type="tensor", wrap_sequence=False)
+        >>> # Converting each element in a list to individual tensors
+        >>> data_list = [1, 2, 3]
+        >>> tensors_list = transform(data_list)
+        >>> tensors_list
+        [tensor(1), tensor(2.), tensor(3)]  # Only second element is float32 rest are int64
     """
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]