*   `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]`

    *   Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops.

*   `tf.nn.embedding_lookup_sparse`

// need for `Env::Default()`. At that time, we might decide to remove the need
// for `Env::Default()` altogether, but that's a different project, not in
// scope for now. I'm just mentioning this here as that transition will mean
// removal of the registration part from `Env` and adding it here instead: we
// will need tables to hold for each scheme the function tables that implement

# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels

# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN
build:windows --copt=-DNOGDI
build:windows --host_copt=-DNOGDI

# MSVC (Windows): Standards-conformant preprocessor mode

# functions (except for tfrun); use dedicated scripts instead and reference them
# specifically. Use your best judgment to keep the scripts in this directory
# lean and easy to follow. When in doubt, remember that for CI scripts, "keep it
# simple" is MUCH more important than "don't repeat yourself."

# -e: abort script if one command fails
# -u: error if undefined variable used