Transforms input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.

input_dataset A Tensor of type variant.
num_parallel_calls A Tensor of type int64.
dense_defaults A list of Tensor objects with types from: float32, int64, string. A dict mapping string keys to Tensors. The keys of the dict must match the dense_keys of the feature.
sparse_keys A list of strings. A list of string keys in the examples features. The results for these keys will be returned as SparseTensor objects.
dense_keys A list of strings. A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values.
sparse_types A list of tf.DTypes from: tf.float32, tf.int64, tf.string. A list of DTypes of the same length as sparse_keys. Only tf.float32 (FloatList), tf.int64 (Int64List), and tf.string (BytesList) are supported.
dense_shapes A list of shapes (each a tf.TensorShape or list of ints). List of tuples with the same length as dense_keys. The shape of the data for each dense feature referenced by dense_keys. Required for any input tensors identified by dense_keys. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension.
output_types A list of tf.DTypes that has length >= 1. The type list for the return values.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
sloppy An optional bool. Defaults to False.
name A name for the operation (optional).

A Tensor of type variant.