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tf.raw_ops.SparseReduceMax

Computes the max of elements across dimensions of a SparseTensor.

This Op takes a SparseTensor and is the sparse counterpart to `tf.reduce_max()`. In particular, this Op also returns a dense `Tensor` instead of a sparse one.

Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained with length 1.

If `reduction_axes` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python.

`input_indices` A `Tensor` of type `int64`. 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering.
`input_values` A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 1-D. `N` non-empty values corresponding to `input_indices`.
`input_shape` A `Tensor` of type `int64`. 1-D. Shape of the input SparseTensor.
`reduction_axes` A `Tensor` of type `int32`. 1-D. Length-`K` vector containing the reduction axes.
`keep_dims` An optional `bool`. Defaults to `False`. If true, retain reduced dimensions with length 1.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `input_values`.

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