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Simply converts the input records (1-D dense tensor) to a sparse tensor.
The name of the tensor indicating which record a slice is from.
The decoded tensors are batch-aligned among themselves, but they don't necessarily have to be batch-aligned with the input records. If not, sub-classes should implement this method to tie the batch dimension with the input record.
The record index tensor must be a SparseTensor or a RaggedTensor of integral type, and must be 2-D and must not contain "missing" values.
A record index tensor like the following: [, , ] means that of 3 "rows" in the output "batch", the first two rows came from the first record, and the 3rd row came from the third record.
The name must not be an empty string.
decode_record( record: tf.Tensor ) -> Dict[Text, Any]
Sub-classes should implement this.
Implementations must use TF ops to derive the result (composite) tensors, as this function will be traced and become a tf.function (thus a TF Graph). Note that autograph is not enabled in such tracing, which means any python control flow / loops will not be converted to TF cond / loops automatically.
The returned tensors must be batch-aligned (i.e. they should be at least
of rank 1, and their outer-most dimensions must be of the same size). They
do not have to be batch-aligned with the input tensor, but if that's the
case, an additional tensor must be provided among the results, to indicate
which input record a "row" in the output batch comes from. See
record_index_tensor_name for more details.
||a 1-D string tensor that contains the records to be decoded.|
|A dict of (composite) tensors.|
output_type_specs() -> Dict[Text, common_types.TensorTypeSpec]
Returns the tf.TypeSpecs of the decoded tensors.
A dict whose keys are the same as keys of the dict returned by