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@ -28,8 +28,9 @@ In this guide, we will:
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Before you begin, make sure you have all the necessary libraries installed:
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```bash
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pip install -q datasets transformers evaluate
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```py
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# uncomment to install the necessary libraries
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!pip install -q datasets transformers evaluate accelerate
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```
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We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
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@ -236,6 +237,9 @@ Then take a look at an example:
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{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x683 at 0x7F9B0C201F90>,
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'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=512x683 at 0x7F9B0C201DD0>,
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'scene_category': 368}
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# view the image
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>>> train_ds[0]["image"]
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```
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- `image`: a PIL image of the scene.
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@ -663,15 +667,19 @@ Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. Y
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</tf>
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</frameworkcontent>
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### Inference
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Great, now that you've finetuned a model, you can use it for inference!
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Load an image for inference:
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Reload the dataset and load an image for inference.
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```py
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>>> image = ds[0]["image"]
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>>> from datasets import load_dataset
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>>> ds = load_dataset("scene_parse_150", split="train[:50]")
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>>> ds = ds.train_test_split(test_size=0.2)
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>>> test_ds = ds["test"]
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>>> image = ds["test"][0]["image"]
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>>> image
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```
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@ -749,7 +757,166 @@ Next, rescale the logits to the original image size and apply argmax on the clas
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</tf>
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</frameworkcontent>
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To visualize the results, load the [dataset color palette](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51) as `ade_palette()` that maps each class to their RGB values. Then you can combine and plot your image and the predicted segmentation map:
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To visualize the results, load the [dataset color palette](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51) as `ade_palette()` that maps each class to their RGB values.
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```py
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def ade_palette():
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return np.asarray([
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[0, 0, 0],
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[120, 120, 120],
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[180, 120, 120],
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[6, 230, 230],
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[80, 50, 50],
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[4, 200, 3],
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[120, 120, 80],
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[140, 140, 140],
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[204, 5, 255],
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[230, 230, 230],
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[4, 250, 7],
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[224, 5, 255],
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[235, 255, 7],
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[150, 5, 61],
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[120, 120, 70],
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[8, 255, 51],
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[255, 6, 82],
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[143, 255, 140],
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[204, 255, 4],
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[255, 51, 7],
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[204, 70, 3],
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[0, 102, 200],
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[61, 230, 250],
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[255, 6, 51],
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[11, 102, 255],
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[255, 7, 71],
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[255, 9, 224],
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[9, 7, 230],
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[220, 220, 220],
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[255, 9, 92],
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[112, 9, 255],
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[8, 255, 214],
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[7, 255, 224],
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[255, 184, 6],
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[10, 255, 71],
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[255, 41, 10],
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[7, 255, 255],
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[224, 255, 8],
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[102, 8, 255],
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[255, 61, 6],
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[255, 194, 7],
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[255, 122, 8],
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[0, 255, 20],
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[255, 8, 41],
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[255, 5, 153],
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[6, 51, 255],
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[235, 12, 255],
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[160, 150, 20],
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[0, 163, 255],
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[140, 140, 140],
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[250, 10, 15],
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[20, 255, 0],
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[31, 255, 0],
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[255, 31, 0],
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[255, 224, 0],
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[153, 255, 0],
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[0, 0, 255],
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[255, 71, 0],
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[0, 235, 255],
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[0, 173, 255],
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[31, 0, 255],
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[11, 200, 200],
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[255, 82, 0],
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[0, 255, 245],
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[0, 61, 255],
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[0, 255, 112],
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[0, 255, 133],
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[255, 0, 0],
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[255, 163, 0],
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[255, 102, 0],
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[194, 255, 0],
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[0, 143, 255],
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[51, 255, 0],
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[0, 82, 255],
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[0, 255, 41],
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[0, 255, 173],
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[10, 0, 255],
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[173, 255, 0],
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[0, 255, 153],
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[255, 92, 0],
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[255, 0, 255],
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[255, 0, 245],
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[255, 0, 102],
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[255, 173, 0],
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[255, 0, 20],
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[255, 184, 184],
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[0, 31, 255],
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[0, 255, 61],
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[0, 71, 255],
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[255, 0, 204],
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[0, 255, 194],
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[0, 255, 82],
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[0, 10, 255],
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[0, 112, 255],
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[51, 0, 255],
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[0, 194, 255],
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[0, 122, 255],
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[0, 255, 163],
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[255, 153, 0],
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[0, 255, 10],
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[255, 112, 0],
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[143, 255, 0],
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[82, 0, 255],
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[163, 255, 0],
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[255, 235, 0],
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[8, 184, 170],
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[133, 0, 255],
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[0, 255, 92],
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[184, 0, 255],
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[255, 0, 31],
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[0, 184, 255],
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[0, 214, 255],
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[255, 0, 112],
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[92, 255, 0],
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[0, 224, 255],
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[112, 224, 255],
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[70, 184, 160],
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[163, 0, 255],
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[153, 0, 255],
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[71, 255, 0],
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[255, 0, 163],
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[255, 204, 0],
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[255, 0, 143],
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[0, 255, 235],
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[133, 255, 0],
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[255, 0, 235],
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[245, 0, 255],
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[255, 0, 122],
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[255, 245, 0],
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[10, 190, 212],
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[214, 255, 0],
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[0, 204, 255],
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[20, 0, 255],
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[255, 255, 0],
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[0, 153, 255],
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[0, 41, 255],
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[0, 255, 204],
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[41, 0, 255],
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[41, 255, 0],
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[173, 0, 255],
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[0, 245, 255],
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[71, 0, 255],
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[122, 0, 255],
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[0, 255, 184],
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[0, 92, 255],
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[184, 255, 0],
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[0, 133, 255],
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[255, 214, 0],
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[25, 194, 194],
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[102, 255, 0],
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[92, 0, 255],
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])
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```
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Then you can combine and plot your image and the predicted segmentation map:
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```py
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>>> import matplotlib.pyplot as plt
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