mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.3.2 \
    --hash=sha256:2c34f2ba9660b21fe1034b608308a01be82bbef2a92fb8199f24dc6bad0d5226 \
    --hash=sha256:3a17ef2322e60858d93584e9c52a5be7dd6236b056b7fa1ec57f1bb6ba043e33 \

numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"
scipy==1.7.3;python_version<"3.11"
ml_dtypes>=0.2.0

# For using Python 3.11 with Bazel 6 (b/286090018)

[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.3.2 \
    --hash=sha256:2c34f2ba9660b21fe1034b608308a01be82bbef2a92fb8199f24dc6bad0d5226 \
    --hash=sha256:3a17ef2322e60858d93584e9c52a5be7dd6236b056b7fa1ec57f1bb6ba043e33 \

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.3.2 \
    --hash=sha256:2c34f2ba9660b21fe1034b608308a01be82bbef2a92fb8199f24dc6bad0d5226 \
    --hash=sha256:3a17ef2322e60858d93584e9c52a5be7dd6236b056b7fa1ec57f1bb6ba043e33 \

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],

        "//third_party/py/cloud_ml_autoflow/...",
        "//third_party/py/envlogger/...",
        "//third_party/py/gldm/...",
        "//third_party/py/guesslang/...",
        "//third_party/py/keras/...",
        "//third_party/py/tf_keras/...",
        "//third_party/yggdrasil_decision_forests/...",
        "//waymo/accelerator/...",
        "//waymo/ml/cn/...",
        "//waymo/ml/models/...",
    ],

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.3.2 \
    --hash=sha256:2c34f2ba9660b21fe1034b608308a01be82bbef2a92fb8199f24dc6bad0d5226 \
    --hash=sha256:3a17ef2322e60858d93584e9c52a5be7dd6236b056b7fa1ec57f1bb6ba043e33 \

            on CPU.
    *   Add nominal support for unsigned 16-bit integer tensor types. Note that
        very few TFLite kernels support this type natively, so its use in mobile
        ML authoring is generally discouraged.
    *   Add support for unsigned 16-bit integer tensor types in cast op.
    *   Experimental support for lowering `list_ops.tensor_list_set_item` with
        `DynamicUpdateSlice`.