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tf.parallel_stack is implemented as a graph rewrite and therefore has no support in eager mode. Added documentation to parallel_stack and raising an error now to reflect that.

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Created a CMakeLists.txt under the lite/examples/minimal

Usage)

$ git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
$ mkdir minimal_build
$ cd miniaml_build
$ cmake ../tensorflow_src/tensorflow/lite/examples/minimal
$ cmake --build . -j

PiperOrigin-RevId: 339977906
Change-Id: I85cada5430407e72354d0a31a17584c48407db2f

When processing buffers that are placed in tuples, we create (possibly
redundant) additional tuple and gte instructions, which we later simplify. We
don't need to create these additional instructions for buffers that were
unchanged after memory space assignment. We were facing compile time and OOM
issues without this change.

PiperOrigin-RevId: 339972236
Change-Id: Ieb82767ed52d39260141f3e3179387fbf5783920

ConvConstants extended for better mapping on thin dst tensors.

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Change-Id: Ia14be12361ec81c70c0522baa9516c8cb28ba7ff

ConvolutionTransposed3x3 generation changed to use storage type properties instead of specific storage types.

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Change-Id: I5f6e192e21cbabf33bd152f91f4ab664b139fd14

Multi-worker tutorial: Add the workflow of MWMS+CTL example that is going to be added in the tutorial in multi_worker_tutorial_test.

Fix the flakiness of the test and re-enable in TAP.

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Change-Id: Icb866f8a7054fa88f2e474c02960982a57c542b3

nest.flatten was mangling some attribute lists

Fixes #40895.

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Change-Id: Ibdbbb5a12b2d06a077b62902764ca4849a2b8d2a

DepthwiseConvolution generation changed to use storage type properties instead of specific storage types.

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Change-Id: I2cded9c306a40b136002c08e610daca2d75e1758

Added A14 GPU to enum.

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Dataset cardinality op should be placed on the same host device as the dataset, otherwise TF will try to encode/decode the dataset to another host device, which is not possible.

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Change-Id: Ifbeb0aabdfc35f6c0e1248d69a8d22727e19c4a2

This further simplifies ShapedBuffer object as itself doesn't have any
logic to use the platform field.

Notice that this cl also removed some sanity check in allocation
tracker. we can add that sanity check back if need --- just keep track
of `platform` inside of allocation tracker as a side map.

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TFRT batch fallback kernel to crash in high QPS load. The bug makes it only
create an object of base class BatchTask even when splitting a task of the
derived class FallbackBatchTask (used only in TFRT), and put it into a batch
with other FallbackBatchTask objects. When this batch of mixed types of tasks is
processed, it crashes.

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Change-Id: I88c0d616154484ddd353d262f2fe7408cfc058c4

#KERAS_PRIVATE_API_CLEANUP

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Change-Id: Iadd13a8d23a941528e0384e090dc07ddc9d63da6

With this change, it is now possible to mix-and-match tf.keras.Layers and
tf.Modules inside a tf.keras.Model and everything will be tracked properly.

- Variables in tf.Modules that are set as attributes of custom Layers and
  Models now show up properly in properties such as Layer.trainable_variables
  and Model.trainable_variables.
- tf.Modules do not show up in Model.layers. Instead, a new method
  Layer._flatten_modules is added that iterates over tf.Modules and Layers in
  the order that Keras expects. The existing method Layer.submodules (inherited
  from tf.Module) can still be used to iterate over tf.Modules and Layer with the
  tf.Module ordering. Layer._flatten_layers is built on top of
  Layer._flatten_modules
- Layer._layers is renamed to Layer._self_tracked_trackables to avoid naming
  conflicts with user-defined attributes (and to reflect that this attr
  contains Layers, Modules, and TrackableDataStructures)
- A new property is added to tf.Module to enable this, namely
  tf.Module.non_trainable_variables

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