parameters: (serialized, reader_schema, features, avro_names=None, name=None)
return_statement: return construct_tensors_for_composite_features(features, outputs)
docstring: Parses `avro` records into a `dict` of tensors.
  
      This op parses serialized avro records into a dictionary mapping keys to
      `Tensor`, and `SparseTensor` objects. `features` is a dict from keys to
      `VarLenFeature`, `SparseFeature`, `RaggedFeature`, and `FixedLenFeature`
      objects. Each `VarLenFeature` and `SparseFeature` is mapped to a
      `SparseTensor`; each `FixedLenFeature` is mapped to a `Tensor`.
  
      Each `VarLenFeature` maps to a `SparseTensor` of the specified type
      representing a ragged matrix. Its indices are `[batch, index]` where `batch`
      identifies the example in `serialized`, and `index` is the value's index in
      the list of values associated with that feature and example.
  
      Each `SparseFeature` maps to a `SparseTensor` of the specified type
      representing a Tensor of `dense_shape` `[batch_size] + SparseFeature.size`.
      Its `values` come from the feature in the examples with key `value_key`.
      A `values[i]` comes from a position `k` in the feature of an example at batch
      entry `batch`. This positional information is recorded in `indices[i]` as
      `[batch, index_0, index_1, ...]` where `index_j` is the `k-th` value of
      the feature in the example at with key `SparseFeature.index_key[j]`.
      In other words, we split the indices (except the first index indicating the
      batch entry) of a `SparseTensor` by dimension into different features of the
      avro record. Due to its complexity a `VarLenFeature` should be preferred
      over a `SparseFeature` whenever possible.
  
      Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
      `tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.
      `FixedLenFeature` entries with a `default_value` are optional. With no default
      value, we will fail if that `Feature` is missing from any example in
      `serialized`.
  
      Use this within the dataset.map(parser_fn=parse_avro).
  
      Only works for batched serialized input!
  
      Args:
          serialized: The batched, serialized string tensors.
  
          reader_schema: The reader schema. Note, this MUST match the reader schema
          from the avro_record_dataset. Otherwise, this op will segfault!
  
          features: A map of feature names mapped to feature information.
  
          avro_names: (Optional.) may contain descriptive names for the
          corresponding serialized avro parts. These may be useful for debugging
          purposes, but they have no effect on the output. If not `None`,
          `avro_names` must be the same length as `serialized`.
  
          name: The name of the op.
  
      Returns:
          A map of feature names to tensors.
docstring_summary: Parses `avro` records into a `dict` of tensors.
docstring_tokens: Parses avro records into a dict of tensors .
function: def parse_avro(serialized, reader_schema, features, avro_names=None, name=None):
      """
      Parses `avro` records into a `dict` of tensors.
  
      This op parses serialized avro records into a dictionary mapping keys to
      `Tensor`, and `SparseTensor` objects. `features` is a dict from keys to
      `VarLenFeature`, `SparseFeature`, `RaggedFeature`, and `FixedLenFeature`
      objects. Each `VarLenFeature` and `SparseFeature` is mapped to a
      `SparseTensor`; each `FixedLenFeature` is mapped to a `Tensor`.
  
      Each `VarLenFeature` maps to a `SparseTensor` of the specified type
      representing a ragged matrix. Its indices are `[batch, index]` where `batch`
      identifies the example in `serialized`, and `index` is the value's index in
      the list of values associated with that feature and example.
  
      Each `SparseFeature` maps to a `SparseTensor` of the specified type
      representing a Tensor of `dense_shape` `[batch_size] + SparseFeature.size`.
      Its `values` come from the feature in the examples with key `value_key`.
      A `values[i]` comes from a position `k` in the feature of an example at batch
      entry `batch`. This positional information is recorded in `indices[i]` as
      `[batch, index_0, index_1, ...]` where `index_j` is the `k-th` value of
      the feature in the example at with key `SparseFeature.index_key[j]`.
      In other words, we split the indices (except the first index indicating the
      batch entry) of a `SparseTensor` by dimension into different features of the
      avro record. Due to its complexity a `VarLenFeature` should be preferred
      over a `SparseFeature` whenever possible.
  
      Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
      `tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.
      `FixedLenFeature` entries with a `default_value` are optional. With no default
      value, we will fail if that `Feature` is missing from any example in
      `serialized`.
  
      Use this within the dataset.map(parser_fn=parse_avro).
  
      Only works for batched serialized input!
  
      Args:
          serialized: The batched, serialized string tensors.
  
          reader_schema: The reader schema. Note, this MUST match the reader schema
          from the avro_record_dataset. Otherwise, this op will segfault!
  
          features: A map of feature names mapped to feature information.
  
          avro_names: (Optional.) may contain descriptive names for the
          corresponding serialized avro parts. These may be useful for debugging
          purposes, but they have no effect on the output. If not `None`,
          `avro_names` must be the same length as `serialized`.
  
          name: The name of the op.
  
      Returns:
          A map of feature names to tensors.
      """
      if not features:
          raise ValueError("Missing: features was %s." % features)
      features = _build_keys_for_sparse_features(features)
      (
          sparse_keys,
          sparse_types,
          sparse_ranks,
          dense_keys,
          dense_types,
          dense_defaults,
          dense_shapes,
      ) = _features_to_raw_params(
          features,
          [
              tensorflow_io.experimental.columnar.VarLenFeatureWithRank,
              tf.io.SparseFeature,
              tf.io.FixedLenFeature,
          ],
      )
  
      outputs = _parse_avro(
          serialized,
          reader_schema,
          avro_names,
          sparse_keys,
          sparse_types,
          sparse_ranks,
          dense_keys,
          dense_types,
          dense_defaults,
          dense_shapes,
          name,
      )
      return construct_tensors_for_composite_features(features, outputs)
function_tokens: def parse_avro ( serialized , reader_schema , features , avro_names = None , name = None ) : if not features : raise ValueError ( "Missing: features was %s." % features ) features = _build_keys_for_sparse_features ( features ) ( sparse_keys , sparse_types , sparse_ranks , dense_keys , dense_types , dense_defaults , dense_shapes , ) = _features_to_raw_params ( features , [ tensorflow_io . experimental . columnar . VarLenFeatureWithRank , tf . io . SparseFeature , tf . io . FixedLenFeature , ] , ) outputs = _parse_avro ( serialized , reader_schema , avro_names , sparse_keys , sparse_types , sparse_ranks , dense_keys , dense_types , dense_defaults , dense_shapes , name , ) return construct_tensors_for_composite_features ( features , outputs )