# Construct a Categorical Column that Returns Identity Values

Use this when your inputs are integers in the range [0, num_buckets), and you want to use the input value itself as the categorical ID. Values outside this range will result in default_value if specified, otherwise it will fail.

column_categorical_with_identity(..., num_buckets, default_value = NULL)

## Arguments

 ... Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns. num_buckets Number of unique values. default_value If NULL, this column's graph operations will fail for out-of-range inputs. Otherwise, this value must be in the range [0, num_buckets), and will replace inputs in that range.

## Value

A categorical column that returns identity values.

## Details

Typically, this is used for contiguous ranges of integer indexes, but it doesn't have to be. This might be inefficient, however, if many of IDs are unused. Consider column_categorical_with_hash_bucket() in that case.

For input dictionary features, features\$key is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

## Raises

• ValueError: if num_buckets is less than one.

• ValueError: if default_value is not in range [0, num_buckets).

Other feature column constructors: column_bucketized, column_categorical_weighted, column_categorical_with_hash_bucket, column_categorical_with_vocabulary_file, column_categorical_with_vocabulary_list, column_crossed, column_embedding, column_numeric, input_layer