A numeric input Tensor or SparseTensor whose values should be mapped
to buckets. For a SparseTensor only non-missing values will be included
in the quantiles computation, and the result of bucketize will be a
SparseTensor with non-missing values mapped to buckets.
Values in the input x are divided into approximately
equal-sized buckets, where the number of buckets is num_buckets. By
default, the exact number will be available to bucketize. If
always_return_num_quantiles is False, the actual number of
buckets computed can be less or more than the requested number. Use the
generated metadata to find the computed number of buckets.
(Optional) Error tolerance, typically a small fraction close to
zero. If a value is not specified by the caller, a suitable value is
computed based on experimental results. For num_buckets less
than 100, the value of 0.01 is chosen to handle a dataset of up to
~1 trillion input data values. If num_buckets is larger,
then epsilon is set to (1/num_buckets) to enforce a stricter
error tolerance, because more buckets will result in smaller range for
each bucket, and so we want the boundaries to be less fuzzy.
See analyzers.quantiles() for details.
(Optional) Weights tensor for the quantiles. Tensor must have the
same shape as x.
(Optional) If true, bucketize each element of the tensor
Deprecated. Do not set.
(Optional) A name for this operation.
A Tensor of the same shape as x, with each element in the
returned tensor representing the bucketized value. Bucketized value is
in the range [0, actual_num_buckets). Sometimes the actual number of buckets
can be different than num_buckets hint, for example in case the number of
distinct values is smaller than num_buckets, or in cases where the
input values are not uniformly distributed.