Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -550,9 +550,10 @@ for i in range(1, len(weights_bias), 2):
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```
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## Type of layers
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## [Type of layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers#classes_2)
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Reduced list with the most relevant network layers
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Reduced list with the most relevant network layers
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|[Activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation)| Applies an activation function to an output.|
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|[Activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation)| Applies an activation function to an output.|
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|[ZeroPadding3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D) |Zero-padding layer for 3D data (spatial or spatio-temporal).|
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|[ZeroPadding3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D) |Zero-padding layer for 3D data (spatial or spatio-temporal).|
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Preprocessing layers
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## [Preprocessing layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers#classes_2)
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Reduced list with the most relevant preprocessing layers
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Reduced list with the most relevant preprocessing layers
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CenterCrop A preprocessing layer which crops images.
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RandomContrast A preprocessing layer which randomly adjusts contrast during training.
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RandomCrop A preprocessing layer which randomly crops images during training.
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|[CenterCrop](https://www.tensorflow.org/api_docs/python/tf/keras/layers/CenterCrop) |A preprocessing layer which crops images.|
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RandomFlip A preprocessing layer which randomly flips images during training.
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|[RandomContrast](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomContrast) |A preprocessing layer which randomly adjusts contrast during training.|
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RandomHeight A preprocessing layer which randomly varies image height during training.
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|[RandomCrop](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop) |A preprocessing layer which randomly crops images during training.|
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RandomRotation A preprocessing layer which randomly rotates images during training.
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|[RandomFlip](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip) |A preprocessing layer which randomly flips images during training.|
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RandomTranslation A preprocessing layer which randomly translates images during training.
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|[RandomHeight](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight)| A preprocessing layer which randomly varies image height during training.|
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RandomWidth A preprocessing layer which randomly varies image width during training.
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|[RandomRotation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation) |A preprocessing layer which randomly rotates images during training.|
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RandomZoom A preprocessing layer which randomly zooms images during training.
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|[RandomTranslation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation) |A preprocessing layer which randomly translates images during training.|
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Rescaling A preprocessing layer which rescales input values to a new range.
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|[RandomWidth](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth) |A preprocessing layer which randomly varies image width during training.|
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Resizing A preprocessing layer which resizes images.
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|[RandomZoom](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom) |A preprocessing layer which randomly zooms images during training.|
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|[Rescaling](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling) |A preprocessing layer which rescales input values to a new range.|
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|[Resizing](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Resizing) |A preprocessing layer which resizes images.|
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Activation functions
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## [Activation functions](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
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Reduced list with the most relevant activation functions
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Reduced list with the most relevant activation functions
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hard_sigmoid(...) Hard sigmoid activation function.
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relu(...) Applies the rectified linear unit activation function.
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|[hard_sigmoid(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/hard_sigmoid)| Hard sigmoid activation function.|
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sigmoid(...) Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).
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|[relu(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu) |Applies the rectified linear unit activation function.|
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softmax(...) Softmax converts a vector of values to a probability distribution.
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|[sigmoid(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid) |Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).|
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softplus(...) Softplus activation function, softplus(x) = log(exp(x) + 1).
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|[softmax(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) |Softmax converts a vector of values to a probability distribution.|
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softsign(...) Softsign activation function, softsign(x) = x / (abs(x) + 1).
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|[softplus(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softplus) |Softplus activation function, softplus(x) = log(exp(x) + 1).|
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tanh(...) Hyperbolic tangent activation function.
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|[softsign(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softsign) |Softsign activation function, softsign(x) = x / (abs(x) + 1).|
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|[tanh(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/tanh) |Hyperbolic tangent activation function.|
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Loss-functions
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## [Loss-functions](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
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Reduced list with the most relevant loss functions
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Reduced list with the most relevant loss functions
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BinaryCrossentropy Computes the cross-entropy loss between true labels and predicted labels.
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CategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
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KLDivergence Computes Kullback-Leibler divergence loss between y_true and y_pred.
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|[BinaryCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) |Computes the cross-entropy loss between true labels and predicted labels.|
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MeanAbsoluteError Computes the mean of absolute difference between labels and predictions.
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|[CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) |Computes the crossentropy loss between the labels and predictions.|
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MeanSquaredError Computes the mean of squares of errors between labels and predictions.
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|[KLDivergence](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLDivergence) |Computes Kullback-Leibler divergence loss between y_true and y_pred.|
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Poisson Computes the Poisson loss between y_true and y_pred.
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|[MeanAbsoluteError](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanAbsoluteError) |Computes the mean of absolute difference between labels and predictions.|
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SparseCategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
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|[MeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanSquaredError) |Computes the mean of squares of errors between labels and predictions.|
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|[Poisson](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Poisson) |Computes the Poisson loss between y_true and y_pred.|
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|[SparseCategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy) |Computes the crossentropy loss between the labels and predictions.|
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Optimizer
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## [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
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Reduced list with the most relevant optimizer
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Reduced list with the most relevant optimizer
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Adagrad Optimizer that implements the Adagrad algorithm.
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Adam Optimizer that implements the Adam algorithm.
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RMSprop Optimizer that implements the RMSprop algorithm.
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|[Adagrad](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Adagrad) |Optimizer that implements the Adagrad algorithm.|
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SGD Gradient descent (with momentum) optimizer.
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|[Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) |Optimizer that implements the Adam algorithm.|
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|[RMSprop](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/RMSprop) |Optimizer that implements the RMSprop algorithm.|
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|[SGD](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/SGD) |Gradient descent (with momentum) optimizer.|
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Metrics
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## [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
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A very reduced list with the most relevant metrics
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A very reduced list with the most relevant metrics
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Accuracy Calculates how often predictions equal labels.
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BinaryAccuracy Calculates how often predictions match binary labels.
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BinaryCrossentropy Computes the crossentropy metric between the labels and predictions.
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|[Accuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy) | Calculates how often predictions equal labels.|
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CategoricalAccuracy Calculates how often predictions match one-hot labels.
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|[BinaryAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryAccuracy) | Calculates how often predictions match binary labels.|
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CategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
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|[BinaryCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
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KLDivergence Computes Kullback-Leibler divergence metric between y_true and y_pred.
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|[CategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy) | Calculates how often predictions match one-hot labels.|
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Mean Computes the (weighted) mean of the given values.
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|[CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
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MeanAbsoluteError Computes the mean absolute error between the labels and predictions.
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|[KLDivergence](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/KLDivergence) | Computes Kullback-Leibler divergence metric between y_true and y_pred.|
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MeanSquaredError Computes the mean squared error between y_true and y_pred.
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|[Mean](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Mean) | Computes the (weighted) mean of the given values.|
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Poisson Computes the Poisson metric between y_true and y_pred.
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|[MeanAbsoluteError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsoluteError) | Computes the mean absolute error between the labels and predictions.|
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Precision Computes the precision of the predictions with respect to the labels.
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|[MeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanSquaredError) | Computes the mean squared error between y_true and y_pred.|
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RootMeanSquaredError Computes root mean squared error metric between y_true and y_pred.
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|[Poisson](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Poisson) | Computes the Poisson metric between y_true and y_pred.|
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SparseCategoricalAccuracy Calculates how often predictions match integer labels.
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|[Precision](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Precision) | Computes the precision of the predictions with respect to the labels.|
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SparseCategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
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|[RootMeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError) | Computes root mean squared error metric between y_true and y_pred.|
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SparseTopKCategoricalAccuracy Computes how often integer targets are in the top K predictions.
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|[SparseCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy) | Calculates how often predictions match integer labels.|
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Sum Computes the (weighted) sum of the given values.
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|[SparseCategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
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TopKCategoricalAccuracy Computes how often targets are in the top K predictions.
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|[SparseTopKCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseTopKCategoricalAccuracy) | Computes how often integer targets are in the top K predictions.|
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|[Sum](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Sum) | Computes the (weighted) sum of the given values.|
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|[TopKCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TopKCategoricalAccuracy)| Computes how often targets are in the top K predictions.|
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```python
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```
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