["python","tensorflow","tensorflow-datasets"]
What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices?

I have a dataset represented as a NumPy matrix of shape (num_features, num_examples) and I wish to convert it to TensorFlow type tf.Dataset. I am struggling trying to understand the difference between these two methods: Dataset.from_tensors and Dataset.from_tensor_slices. What is the right one and why? TensorFlow documentation (link) says that both method accept a nested structure of tensor although when using from_tensor_slices the tensor should have same size in the 0-th dimension.
from_tensors combines the input and returns a dataset with a single element: >>> t = tf.constant([[1, 2], [3, 4]]) >>> ds = tf.data.Dataset.from_tensors(t) >>> [x for x in ds] [<tf.Tensor: shape=(2, 2), dtype=int32, numpy= array([[1, 2], [3, 4]], dtype=int32)>] from_tensor_slices creates a dataset with a separate element for each row of the input tensor: >>> t = tf.constant([[1, 2], [3, 4]]) >>> ds = tf.data.Dataset.from_tensor_slices(t) >>> [x for x in ds] [<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>, <tf.Tensor: shape=(2,), dtype=int32, numpy=array([3, 4], dtype=int32)>]