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fix linting
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README.md

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@@ -420,16 +420,16 @@ indicate `dim_order = ["batch", "channel", "height", "width"]`.
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Select one option from:
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| `type`<br>([Scaling Enum](#scaling-enum)) | Required Properties | Scaling Operation |
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|-------------------------------------------|-------------------------------------------------|------------------------------------------|
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| `min-max` | `minimum`, `maximum` | $(data - minimum) / (maximum - minimum)$ |
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| `z-score` | `mean`, `stddev` | $(data - mean) / stddev$ |
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| `clip` | `minimum`, `maximum` | $\min(\max(data, minimum), maximum)$ |
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| `clip-min` | `minimum` | $\max(data, minimum)$ |
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| `clip-max` | `maximum` | $\min(data, maximum)$ |
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| `offset` | `value` | $data - value$ |
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| `scale` | `value` | $data / value$ |
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| `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* |
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| `type` | Required Properties | Scaling Operation |
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|--------------|-------------------------------------------------|------------------------------------------|
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| `min-max` | `minimum`, `maximum` | $(data - minimum) / (maximum - minimum)$ |
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| `z-score` | `mean`, `stddev` | $(data - mean) / stddev$ |
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| `clip` | `minimum`, `maximum` | $\min(\max(data, minimum), maximum)$ |
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| `clip-min` | `minimum` | $\max(data, minimum)$ |
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| `clip-max` | `maximum` | $\min(data, maximum)$ |
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| `offset` | `value` | $data - value$ |
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| `scale` | `value` | $data / value$ |
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| `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* |
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When a scaling `type` approach is specified, it is expected that the parameters necessary
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to perform their calculation are provided for the corresponding input dimension data.
@@ -458,16 +458,14 @@ the [Scaling Object](#scaling-object) of the respective input dimension, as show
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}
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```
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For operations such as L1 or L2 normalization, a [Processing Expression](#processing-expression) should also be employed.
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For operations such as L1 or L2 normalization, [Processing Expression](#processing-expression) should also be employed.
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This is because, depending on the [Model Input](#model-input-object) dimensions and reference data, there is an
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ambiguity regarding "how" and "where" such normalization functions must be applied against the input data.
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A custom mathematical expression should provide explicitly the data manipulation and normalization strategy.
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In situations of very complex `scaling` operations, which cannot be represented by any of the previous definition,
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a `pre_processing_function` should be used instead (see [Model Input Object](#model-input-object)).
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[opencv-normalization-flags]: https://docs.opencv.org/4.x/d2/de8/group__core__array.html#gad12cefbcb5291cf958a85b4b67b6149f
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#### Resize Enum
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Select one option from:
@@ -486,7 +484,7 @@ See [OpenCV - Interpolation Flags][opencv-interpolation-flags]
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for details about the relevant methods. Equivalent methods from other packages are applicable as well.
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If none of the above values applies, `null` (literal, not string) can be used instead.
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If a custom rescaling operation, or a combination of operations (with or without [Normalization](#normalize-enum)),
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If a custom rescaling operation, or a combination of operations (with or without [Scaling](#scaling-object)),
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must be defined instead, consider using a [Processing Expression](#processing-expression) reference.
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[opencv-interpolation-flags]: https://docs.opencv.org/4.x/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121

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