tfx_bsl.public.tfxio.TFGraphRecordDecoder

Base class for decoders that turns a list of bytes to (composite) tensors.

Sub-classes must implement decode_record() (see its docstring for requirements).

Decoder instances can be saved as a SavedModel by save_decoder(). The SavedModel can be loaded back by load_decoder(). However, the loaded decoder will always be of the type LoadedDecoder and only have the public interfaces listed in this base class available.

Attributes
`record_index_tensor_name`	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: [[0], [0], [2]] 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.

Attributes

record_index_tensor_name

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: [[0], [0], [2]] 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.

Methods

`decode_record`

@abc.abstractmethod
decode_record(
    records: tf.Tensor
) -> Dict[Text, TensorAlike]

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.

Args
`records`	a 1-D string tensor that contains the records to be decoded.

Returns
A dict of (composite) tensors.

`output_type_specs`

output_type_specs() -> Dict[Text, tf.TypeSpec]

Returns the tf.TypeSpecs of the decoded tensors.

Returns
A dict whose keys are the same as keys of the dict returned by `decode_record()` and values are the tf.TypeSpec of the corresponding (composite) tensor.