parameters: (serialized,
                                          feature_columns,
                                          name=None,
                                          example_names=None)
return_statement: return columns_to_tensors
docstring: Parses tf.Examples to extract tensors for given feature_columns.
  
    This is a wrapper of 'tf.parse_example'. A typical usage is as follows:
  
    ```python
    columns_to_tensor = parse_feature_columns_from_examples(
        serialized=my_data,
        feature_columns=my_features)
  
    # Where my_features are:
    # Define features and transformations
    country = sparse_column_with_keys(column_name="native_country",
                                      keys=["US", "BRA", ...])
    country_emb = embedding_column(sparse_id_column=country, dimension=3,
                                   combiner="sum")
    occupation = sparse_column_with_hash_bucket(column_name="occupation",
                                                hash_bucket_size=1000)
    occupation_emb = embedding_column(sparse_id_column=occupation, dimension=16,
                                     combiner="sum")
    occupation_x_country = crossed_column(columns=[occupation, country],
                                          hash_bucket_size=10000)
    age = real_valued_column("age")
    age_buckets = bucketized_column(
        source_column=age,
        boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
  
    my_features = [occupation_emb, age_buckets, country_emb]
    ```
  
    Args:
      serialized: A vector (1-D Tensor) of strings, a batch of binary
        serialized `Example` protos.
      feature_columns: An iterable containing all the feature columns. All items
        should be instances of classes derived from _FeatureColumn.
      name: A name for this operation (optional).
      example_names: A vector (1-D Tensor) of strings (optional), the names of
        the serialized protos in the batch.
  
    Returns:
      A `dict` mapping FeatureColumn to `Tensor` and `SparseTensor` values.
docstring_summary: Parses tf.Examples to extract tensors for given feature_columns.
docstring_tokens: Parses tf . Examples to extract tensors for given feature_columns .
function: def parse_feature_columns_from_examples(serialized,
                                          feature_columns,
                                          name=None,
                                          example_names=None):
    """Parses tf.Examples to extract tensors for given feature_columns.
  
    This is a wrapper of 'tf.parse_example'. A typical usage is as follows:
  
    ```python
    columns_to_tensor = parse_feature_columns_from_examples(
        serialized=my_data,
        feature_columns=my_features)
  
    # Where my_features are:
    # Define features and transformations
    country = sparse_column_with_keys(column_name="native_country",
                                      keys=["US", "BRA", ...])
    country_emb = embedding_column(sparse_id_column=country, dimension=3,
                                   combiner="sum")
    occupation = sparse_column_with_hash_bucket(column_name="occupation",
                                                hash_bucket_size=1000)
    occupation_emb = embedding_column(sparse_id_column=occupation, dimension=16,
                                     combiner="sum")
    occupation_x_country = crossed_column(columns=[occupation, country],
                                          hash_bucket_size=10000)
    age = real_valued_column("age")
    age_buckets = bucketized_column(
        source_column=age,
        boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
  
    my_features = [occupation_emb, age_buckets, country_emb]
    ```
  
    Args:
      serialized: A vector (1-D Tensor) of strings, a batch of binary
        serialized `Example` protos.
      feature_columns: An iterable containing all the feature columns. All items
        should be instances of classes derived from _FeatureColumn.
      name: A name for this operation (optional).
      example_names: A vector (1-D Tensor) of strings (optional), the names of
        the serialized protos in the batch.
  
    Returns:
      A `dict` mapping FeatureColumn to `Tensor` and `SparseTensor` values.
    """
    check_feature_columns(feature_columns)
    columns_to_tensors = parsing_ops.parse_example(
        serialized=serialized,
        features=fc.create_feature_spec_for_parsing(feature_columns),
        name=name,
        example_names=example_names)
  
    transformer = _Transformer(columns_to_tensors)
    for column in sorted(set(feature_columns), key=lambda x: x.key):
      transformer.transform(column)
    return columns_to_tensors
function_tokens: def parse_feature_columns_from_examples ( serialized , feature_columns , name = None , example_names = None ) : check_feature_columns ( feature_columns ) columns_to_tensors = parsing_ops . parse_example ( serialized = serialized , features = fc . create_feature_spec_for_parsing ( feature_columns ) , name = name , example_names = example_names ) transformer = _Transformer ( columns_to_tensors ) for column in sorted ( set ( feature_columns ) , key = lambda x : x . key ) : transformer . transform ( column ) return columns_to_tensors