A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
Tensor objects, and feature columns.
An iterable of integers specifies the shape of the Tensor. An
integer can be given which means a single dimension Tensor with given
width. The Tensor representing the column will have the shape of
[batch_size] + shape.
A single value compatible with dtype or an iterable of
values compatible with dtype which the column takes on during
tf.Example parsing if data is missing. A default value of None will
cause tf.io.parse_example to fail if an example does not contain this
column. If a single value is provided, the same value will be applied as
the default value for every item. If an iterable of values is provided,
the shape of the default_value should be equal to the given shape.
defines the type of values. Default value is tf.float32. Must be a
non-quantized, real integer or floating point type.
If not None, a function that can be used to normalize the
value of the tensor after default_value is applied for parsing.
Normalizer function takes the input Tensor as its argument, and returns
the output Tensor. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
even though the most common use case of this function is normalization, it
can be used for any kind of Tensorflow transformations.
if any dimension in shape is not an int
if any dimension in shape is not a positive integer
if default_value is an iterable but not compatible with shape