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Creates parsing spec dictionary from input feature_columns.

The returned dictionary can be used as arg 'features' in

Typical usage example:

# Define features and transformations
feature_a = categorical_column_with_vocabulary_file(...)
feature_b = numeric_column(...)
feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...)
feature_a_x_feature_c = crossed_column(
    columns=["feature_a", feature_c_bucketized], ...)

feature_columns = set(
    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
features =

For the above example, make_parse_example_spec would return the dict:

    "feature_a": parsing_ops.VarLenFeature(tf.string),
    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)

feature_columns An iterable containing all feature columns. All items should be instances of classes derived from _FeatureColumn.

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

ValueError If any of the given feature_columns is not a _FeatureColumn instance.