Heap buffer overflow in `RaggedBinCount`

TensorFlow is an end-to-end open source platform for machine learning. If the splits argument of RaggedBincount does not specify a valid SparseTensor(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the splits tensor buffer in the implementation of the RaggedBincount op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the for loop, batch_idx is set to 0. The user controls the splits array, making it contain only one element, 0. Thus, the code in the while loop would increment batch_idx and then try to read splits(1), which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.