absl::Span<AbstractTensorHandle*> outputs, bool use_function);

Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx);

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
Status TestTensorHandleWithDims(AbstractContext* ctx, const T* data,
                                const int64_t* dims, int num_dims,

    given-names: Lukasz
  - family-names: Kudlur
    given-names: Manjunath
  - family-names: Levenberg
    given-names: Josh
  - family-names: Mané
    given-names: Dan
  - family-names: Schuster
    given-names: Mike
  - family-names: Monga
    given-names: Rajat
  - family-names: Moore
    given-names: Sherry
  - family-names: Murray
    given-names: Derek
  - family-names: Olah
    given-names: Chris

  // Sets the operation device name.
  //
  // The given `name` must be parseable by DeviceNameUtils::ParseFullName, and
  // the result will be used as a constraint for device placement. See the
  // documentation for DeviceName for more details.
  //
  // The value will override the previous value - that is, no "merging" of
  // existing and given constraints will be performed.
  virtual Status SetDeviceName(const char* name) = 0;

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# setup.packages.sh: Given a list of Ubuntu packages, install them and clean up.
# Usage: setup.packages.sh <package_list.txt>
set -e

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

dependencies are always used. \
See https://github.com/bazelbuild/rules_python/ for more details.

### Specifying the Python version

Note: Only a number of minor Python versions are supported at any given time.

By default, the lowest supported version is used.

To set a different version, use the `TF_PYTHON_VERSION` environment variable,
e.g.

```
export TF_PYTHON_VERSION=3.11
```

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a BiasAdd: A + bias, the gradients are:
     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];

TF_AbstractFunction* TF_FinalizeFunction(TF_ExecutionContext* ctx,
                                         TF_OutputList*, TF_Status*);

void TF_DeleteAbstractFunction(TF_AbstractFunction*);

// Register the function with the given context. This is particularly useful for
// making a function available to an eager context.
void TF_ExecutionContextRegisterFunction(TF_ExecutionContext*,
                                         TF_AbstractFunction*, TF_Status*);

// Return a tensor handle containing 2D matrix containing given data and
// dimensions
TFE_TensorHandle* TestMatrixTensorHandleWithInput(TFE_Context* ctx,
                                                  float data[], int64_t dims[],
                                                  int num_dims);

// Get a Matrix TensorHandle with given float values and dimensions