Repository: Project-MONAI/MONAI
File: monai/auto3dseg/analyzer.py (Python)

Code before:
            ImageStatsKeys.INTENSITY: None,
        }

        super().__init__(stats_name, report_format)
        self.update_ops(ImageStatsKeys.INTENSITY, SampleOperations())

    def __call__(self, data):
        # Input Validation Addition
        if not isinstance(data, dict):
            raise TypeError(f"Input data must be a dict, but got {type(data).__name__}.")
        if self.image_key not in data:
            raise KeyError(f"Key '{self.image_key}' not found in input data.")
        image = data[self.image_key]
        if not isinstance(image, (np.ndarray, torch.Tensor, MetaTensor)):
            raise TypeError(
                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
                f"but got {type(image).__name__}."
            )
        if image.ndim < 3:
            raise ValueError(
                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
                f"but got {type(image).__name__}."
            )
        if image.ndim < 3:
            raise ValueError(
                f"Image data under '{self.image_key}' must have at least 3 dimensions, " f"but got shape {image.shape}."
            )
            # --- End of validation ---
        """
        Callable to execute the pre-defined functions

        Returns:
            A dictionary. The dict has the key in self.report_format. The value of
            ImageStatsKeys.INTENSITY is in a list format. Each element of the value list
            has stats pre-defined by SampleOperations (max, min, ....).

        Raises:
            RuntimeError if the stats report generated is not consistent with the pre-
                defined report_format.

        Note:
            The stats operation uses numpy and torch to compute max, min, and other
            functions. If the input has nan/inf, the stats results will be nan/inf.

        """
        d = dict(data)
        start = time.time()
        restore_grad_state = torch.is_grad_enabled()
        torch.set_grad_enabled(False)

        ndas = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])]

Review comment (suggestion):
```suggestion
                f"Image data under '{self.image_key}' must have at least 3 dimensions, but got shape {image.shape}."
```

Code after:
            ImageStatsKeys.INTENSITY: None,
        }

        super().__init__(stats_name, report_format)
        self.update_ops(ImageStatsKeys.INTENSITY, SampleOperations())

    def __call__(self, data):
        # Input Validation Addition
        if not isinstance(data, dict):
            raise TypeError(f"Input data must be a dict, but got {type(data).__name__}.")
        if self.image_key not in data:
            raise KeyError(f"Key '{self.image_key}' not found in input data.")
        image = data[self.image_key]
        if not isinstance(image, (np.ndarray, torch.Tensor, MetaTensor)):
            raise TypeError(
                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
                f"but got {type(image).__name__}."
            )
        if image.ndim < 3:
            raise ValueError(
                f"Image data under '{self.image_key}' must have at least 3 dimensions, but got shape {image.shape}."
            )
            # --- End of validation ---
        """
        Callable to execute the pre-defined functions

        Returns:
            A dictionary. The dict has the key in self.report_format. The value of
            ImageStatsKeys.INTENSITY is in a list format. Each element of the value list
            has stats pre-defined by SampleOperations (max, min, ....).

        Raises:
            RuntimeError if the stats report generated is not consistent with the pre-
                defined report_format.

        Note:
            The stats operation uses numpy and torch to compute max, min, and other
            functions. If the input has nan/inf, the stats results will be nan/inf.

        """
        d = dict(data)
        start = time.time()
        restore_grad_state = torch.is_grad_enabled()
        torch.set_grad_enabled(False)

        ndas = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])]
        if "nda_croppeds" not in d:
            nda_croppeds = [get_foreground_image(nda) for nda in ndas]

        # perform calculation
        report = deepcopy(self.get_report_format())