Add notebook showing how to customize prepare_inputs

[Copilot]

Change Description

Adds a notebook demonstrating how to swap the prepare_inputs static method on a model class to customize input preprocessing.

Solution Description

New notebook docs/notebooks/custom_prepare_inputs.ipynb walks through:

• Loading a model class via h.model() and inspecting its default prepare_inputs
• Four practical examples: uint16 normalization, log-scaling fluxes, single-channel selection, image-only (autoencoder) mode
• Key gotchas: return NumPy not tensors, import inside the function body for save/load portability

ModelClass = h.model()

@staticmethod
def prepare_inputs_normalized(data_dict):
    import numpy as np
    data = data_dict["data"]
    image = np.asarray(data["image"], dtype=np.float32) / 65535.0
    label = np.asarray(data.get("label", []), dtype=np.int64)
    return (image, label)

ModelClass.prepare_inputs = prepare_inputs_normalized

Also adds the notebook entry to docs/common_workflows.rst.

Code Quality

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 64.85%. Comparing base (f0bb945) to head (3214521).

Additional details and impacted files

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  Coverage   64.85%   64.85%           
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  Files          61       61           
  Lines        5899     5899           
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  Hits         3826     3826           
  Misses       2073     2073           

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