AnandInguva opened a new issue, #24902:
URL: https://github.com/apache/beam/issues/24902
### What needs to happen?
This function should accept a rank 2 SparseTensor, and a default value, and
return a rank 1 Tensor. It will assume the input is from a VarLenFeature and
has dimensions [batch_size, 0] or [batch_size, 1] depending on the max size of
the feature over the batch. It's assumed each feature has 0 or 1 values (0 for
missing, 1 for present).
It will emit a Tensor which is constructed using the code
feature = tf.sparse_to_dense(
feature.indices, [feature.dense_shape[0], 1], feature.values,
default_value=-1)
feature = tf.squeeze(feature, axis=1)
### Issue Priority
Priority: 3 (nice-to-have improvement)
### Issue Components
- [X] Component: Python SDK
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- [ ] Component: IO connector
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