Less verbose meta tensor print (#5658)

### Description

We've had feedback that with MetaTensors, `x.__repr__` is overly
verbose. This is used frequently when debugging with an IDE for example.
The problem is accentuated with e.g., `x.max()`. Here, a single value is
hidden by over 50 lines of text.

Example:

```python
import monai.transforms as mt
loader = mt.LoadImage(image_only=True)
x = loader(x)
print(x.max().__repr__())
```

<details>
  <summary>Output</summary>

```
tensor(2906.1714)
Metadata
	sizeof_hdr: 348
	extents: 0
	session_error: 0
	dim_info: 0
	dim: [  3 161 338  61   1   1   1   1]
	intent_p1: 0.0
	intent_p2: 0.0
	intent_p3: 0.0
	intent_code: 0
	datatype: 16
	bitpix: 32
	slice_start: 0
	pixdim: [1.       1.       1.       2.000296 0.       0.       0.       0.      ]
	vox_offset: 0.0
	scl_slope: nan
	scl_inter: nan
	slice_end: 0
	slice_code: 0
	xyzt_units: 2
	cal_max: 0.0
	cal_min: 0.0
	slice_duration: 0.0
	toffset: 0.0
	glmax: 0
	glmin: 0
	qform_code: 1
	sform_code: 0
	quatern_b: -0.4889228641986847
	quatern_c: 0.5108369588851929
	quatern_d: -0.4703003466129303
	qoffset_x: -77.69402313232422
	qoffset_y: 218.0015411376953
	qoffset_z: -808.5780029296875
	srow_x: [0. 0. 0. 0.]
	srow_y: [0. 0. 0. 0.]
	srow_z: [0. 0. 0. 0.]
	affine: tensor([[ 3.5726e-02, -2.8526e-03,  1.9990e+00, -7.7694e+01],
        [-9.9619e-01,  7.9544e-02,  7.1691e-02,  2.1800e+02],
        [-7.9595e-02, -9.9683e-01,  1.3722e-07, -8.0858e+02],
        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],
       dtype=torch.float64)
	original_affine: [[ 3.57263708e-02 -2.85262855e-03  1.99901100e+00 -7.76940231e+01]
 [-9.96186848e-01  7.95440337e-02  7.16907652e-02  2.18001541e+02]
 [-7.95951681e-02 -9.96827272e-01  1.37223043e-07 -8.08578003e+02]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]
	as_closest_canonical: False
	spatial_shape: [161 338  61]
	space: RAS
	original_channel_dim: no_channel
	filename_or_obj: /Users/rich/Downloads/sub-verse004_ct.nii.gz

Applied operations
[]
Is batch?: False
```
</details>

I'm proposing that the `__repr__` and `__str__` match that of
`torch.Tensor`. If users want meta data they can use `x.print_verbose()`
or `print(x.meta)`.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [x] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [x] In-line docstrings updated.
- [x] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>

Author: rijobro

monai/data/meta_tensor.py

@@ -269,9 +269,6 @@ def __torch_function__(cls, func, types, args=(), kwargs=None) -> Any:
         # if `out` has been used as argument, metadata is not copied, nothing to do.
         # if "out" in kwargs:
         #     return ret
-        # we might have 1 or multiple outputs. Might be MetaTensor, might be something
-        # else (e.g., `__repr__` returns a string).
-        # Convert to list (if necessary), process, and at end remove list if one was added.
         if _not_requiring_metadata(ret):
             return ret
         if _get_named_tuple_like_type(func) is not None and isinstance(ret, _get_named_tuple_like_type(func)):
@@ -281,6 +278,9 @@ def __torch_function__(cls, func, types, args=(), kwargs=None) -> Any:
                 ret[idx].meta = out_items[idx].meta
                 ret[idx].applied_operations = out_items[idx].applied_operations
             return ret
+        # we might have 1 or multiple outputs. Might be MetaTensor, might be something
+        # else (e.g., `__repr__` returns a string).
+        # Convert to list (if necessary), process, and at end remove list if one was added.
         if not isinstance(ret, Sequence):
             ret = [ret]
             unpack = True
@@ -532,17 +532,22 @@ def ensure_torch_and_prune_meta(
         # return the `MetaTensor`
         return MetaTensor(img, meta=meta)
 
-    def __repr__(self, *, tensor_contents=None):
+    def __repr__(self):
         """
-        Prints out a long representation of the MetaTensor object with metadata as well as content data.
-
-        Args:
-            tensor_contents: currently unused
+        Prints a representation of the tensor identical to ``torch.Tensor.__repr__``.
+        Use ``print_verbose`` for associated metadata.
         """
-        return self.as_tensor().__repr__() + super().__repr__()
+        return self.as_tensor().__repr__()
 
     def __str__(self):
         """
-        Prints a simpler representation of the tensor identical to torch.Tensor.__str__.
+        Prints a representation of the tensor identical to ``torch.Tensor.__str__``.
+        Use ``print_verbose`` for associated metadata.
         """
         return str(self.as_tensor())
+
+    def print_verbose(self) -> None:
+        """Verbose print with meta data."""
+        print(self)
+        if self.meta is not None:
+            print(self.meta.__repr__())

tests/test_meta_tensor.py

@@ -423,6 +423,7 @@ def test_decollate(self, dtype):
     def test_str(self):
         t = MetaTensor([1.0], affine=torch.tensor(1), meta={"fname": "filename"})
         self.assertEqual(str(t), "tensor([1.])")
+        self.assertEqual(t.__repr__(), "tensor([1.])")
 
     def test_shape(self):
         s = MetaTensor([1])