tf.raw_ops.LegacyParallelInterleaveDatasetV2

Creates a dataset that applies f to the outputs of input_dataset.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.LegacyParallelInterleaveDatasetV2

tf.raw_ops.LegacyParallelInterleaveDatasetV2(
    input_dataset,
    other_arguments,
    cycle_length,
    block_length,
    buffer_output_elements,
    prefetch_input_elements,
    f,
    output_types,
    output_shapes,
    deterministic='default',
    metadata='',
    name=None
)

The resulting dataset is similar to the InterleaveDataset, with the exception that if retrieving the next value from a dataset would cause the requester to block, it will skip that input dataset. This dataset is especially useful when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it allows the training step to proceed so long as some data is available.

!! WARNING !! This dataset is not deterministic!

Args
input_dataset	A Tensor of type variant.
other_arguments	A list of Tensor objects.
cycle_length	A Tensor of type int64.
block_length	A Tensor of type int64.
buffer_output_elements	A Tensor of type int64.
prefetch_input_elements	A Tensor of type int64.
f	A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
output_types	A list of tf.DTypes that has length >= 1.
output_shapes	A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
deterministic	An optional string. Defaults to "default".
metadata	An optional string. Defaults to "".
name	A name for the operation (optional).

Returns
A Tensor of type variant.