parameters: (categorical_column,
                                  weight_feature_key,
                                  dtype=dtypes.float32)
return_statement: return WeightedCategoricalColumn(
        categorical_column=categorical_column,
        weight_feature_key=weight_feature_key,
        dtype=dtype)
docstring: Applies weight values to a `CategoricalColumn`.
  
    Use this when each of your sparse inputs has both an ID and a value. For
    example, if you're representing text documents as a collection of word
    frequencies, you can provide 2 parallel sparse input features ('terms' and
    'frequencies' below).
  
    Example:
  
    Input `tf.Example` objects:
  
    ```proto
    [
      features {
        feature {
          key: "terms"
          value {bytes_list {value: "very" value: "model"}}
        }
        feature {
          key: "frequencies"
          value {float_list {value: 0.3 value: 0.1}}
        }
      },
      features {
        feature {
          key: "terms"
          value {bytes_list {value: "when" value: "course" value: "human"}}
        }
        feature {
          key: "frequencies"
          value {float_list {value: 0.4 value: 0.1 value: 0.2}}
        }
      }
    ]
    ```
  
    ```python
    categorical_column = categorical_column_with_hash_bucket(
        column_name='terms', hash_bucket_size=1000)
    weighted_column = weighted_categorical_column(
        categorical_column=categorical_column, weight_feature_key='frequencies')
    columns = [weighted_column, ...]
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    linear_prediction, _, _ = linear_model(features, columns)
    ```
  
    This assumes the input dictionary contains a `SparseTensor` for key
    'terms', and a `SparseTensor` for key 'frequencies'. These 2 tensors must have
    the same indices and dense shape.
  
    Args:
      categorical_column: A `CategoricalColumn` created by
        `categorical_column_with_*` functions.
      weight_feature_key: String key for weight values.
      dtype: Type of weights, such as `tf.float32`. Only float and integer weights
        are supported.
  
    Returns:
      A `CategoricalColumn` composed of two sparse features: one represents id,
      the other represents weight (value) of the id feature in that example.
  
    Raises:
      ValueError: if `dtype` is not convertible to float.
docstring_summary: Applies weight values to a `CategoricalColumn`.
docstring_tokens: Applies weight values to a CategoricalColumn .
function: def weighted_categorical_column(categorical_column,
                                  weight_feature_key,
                                  dtype=dtypes.float32):
    """Applies weight values to a `CategoricalColumn`.
  
    Use this when each of your sparse inputs has both an ID and a value. For
    example, if you're representing text documents as a collection of word
    frequencies, you can provide 2 parallel sparse input features ('terms' and
    'frequencies' below).
  
    Example:
  
    Input `tf.Example` objects:
  
    ```proto
    [
      features {
        feature {
          key: "terms"
          value {bytes_list {value: "very" value: "model"}}
        }
        feature {
          key: "frequencies"
          value {float_list {value: 0.3 value: 0.1}}
        }
      },
      features {
        feature {
          key: "terms"
          value {bytes_list {value: "when" value: "course" value: "human"}}
        }
        feature {
          key: "frequencies"
          value {float_list {value: 0.4 value: 0.1 value: 0.2}}
        }
      }
    ]
    ```
  
    ```python
    categorical_column = categorical_column_with_hash_bucket(
        column_name='terms', hash_bucket_size=1000)
    weighted_column = weighted_categorical_column(
        categorical_column=categorical_column, weight_feature_key='frequencies')
    columns = [weighted_column, ...]
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    linear_prediction, _, _ = linear_model(features, columns)
    ```
  
    This assumes the input dictionary contains a `SparseTensor` for key
    'terms', and a `SparseTensor` for key 'frequencies'. These 2 tensors must have
    the same indices and dense shape.
  
    Args:
      categorical_column: A `CategoricalColumn` created by
        `categorical_column_with_*` functions.
      weight_feature_key: String key for weight values.
      dtype: Type of weights, such as `tf.float32`. Only float and integer weights
        are supported.
  
    Returns:
      A `CategoricalColumn` composed of two sparse features: one represents id,
      the other represents weight (value) of the id feature in that example.
  
    Raises:
      ValueError: if `dtype` is not convertible to float.
    """
    if (dtype is None) or not (dtype.is_integer or dtype.is_floating):
      raise ValueError('dtype {} is not convertible to float.'.format(dtype))
    return WeightedCategoricalColumn(
        categorical_column=categorical_column,
        weight_feature_key=weight_feature_key,
        dtype=dtype)
function_tokens: def weighted_categorical_column ( categorical_column , weight_feature_key , dtype = dtypes . float32 ) : if ( dtype is None ) or not ( dtype . is_integer or dtype . is_floating ) : raise ValueError ( 'dtype {} is not convertible to float.' . format ( dtype ) ) return WeightedCategoricalColumn ( categorical_column = categorical_column , weight_feature_key = weight_feature_key , dtype = dtype )