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        "description": "=====================\nDAM4ML CLIENT LIBRARY\n=====================\n\nThis is the dam4ml client library.\n\nInstallation\n------------\n\npip install dam4ml\n\n(Ugh, pretty is, uh?)\n\nBasic usage\n-----------\n\n.. code-block:: python\n\n    from dam4ml import client\n    from dam4ml import transforms\n\n    # Login to DAM4ML\n    dataset = client.connect(\"mnist\", api_key=\"\")\n\n    # (optional) Pre-load the whole dataset for offline performance.\n    # This will take a while but will improve further performance.\n    dataset.load()\n\n    # (optional) You can pre-filter your dataset. See DAM4ML website\n    # for more information about how to build your filter\n    filter = {\n        \"tag_slug\": \"test\",\n    }\n\n    # Iterate through all dataset items\n    for item in dataset.as_dict(**filter):\n        # ...process each dataset item here.\n        pass\n\n    # Convert dataset to a pynum array\n    dataset.as_pynum(**filter)\n\n    # Even better, simulate what Keras' load_dataset() method would do:\n    pn_dataset = dataset.as_pynum()\n    (x_train, y_train) = pn_dataset[]\n    (x_val, y_val) = pn_dataset[]\n\n\n\n\n",
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