# We have some invalid linker scripts in the build,
# so we need to disable this check
build:release_linux_base --linkopt=-Wl,--undefined-version

# Container environment settings below this point.
# Use Python 3.X as installed in container image
build:release_linux_base --action_env PYTHON_BIN_PATH="/usr/bin/python3"
build:release_linux_base --action_env PYTHON_LIB_PATH="/usr/lib/tf_python"

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

  // a tensor with the result.
  virtual Gradient* AggregateGradients(
      gtl::ArraySlice<Gradient*> gradient_tensors) const = 0;

  // Calls the passed-in backward function.
  //
  // `unneeded_gradients` contains sorted list of input indices for which a
  // gradient is not required.
  virtual Status CallBackwardFunction(
      const string& op_type, BackwardFunction* backward_function,

The CI folder contains the configuration files and scripts used to build, test,
and deploy TensorFlow. This folder is typically used by continuous integration
(CI) tools to build and test TensorFlow whenever there is a change to the
code. This folder is broken into subfolders that represent the level of support

# JAX requirements, passed into container by defining the ARG 
# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"

  auto cc_status =
      cc_ctx->resource_manager()->Create<tensorflow::PluginResource>(
          container_name, plugin_resource_name, cc_resource_ptr);
  status->status = cc_status;
}

void TF_LookupOrCreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void** result_plugin_resource,
    void* (*create_func)(void*), void* create_func_args,

//           `inputs` cannot contain the same tensor twice.
//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.

// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };

Usage: squash_testlogs.py START_DIRECTORY OUTPUT_FILE

Example: squash_testlogs.py /tf/pkg/testlogs /tf/pkg/merged.xml

Recursively find all the JUnit test.xml files in one directory, and merge any
of them that contain failures into one file. The TensorFlow DevInfra team
uses this to generate a simple overview of an entire pip and nonpip test
invocation, since the normal logs that Bazel creates are too large for the
internal invocation viewer.

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradients.h"

#include <memory>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_experimental.h"