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,

  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];

// Given a Tensor, wrap it with a TensorHandle
//
// Similar to TFE_NewTensorHandle, but includes a pointer to the TFE_Context.
// The context should be identical to that of the Tensor.
TF_CAPI_EXPORT TFE_TensorHandle* TFE_NewTensorHandleFromTensor(
    TFE_Context* ctx, TF_Tensor* t, TF_Status* status);

// Create a packed TensorHandle with the given list of TensorHandles.

    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

      }
    }
  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a matmul op A*B, the gradients are:
     *
     *    dA = U * B.T
     *    dB = A.T * U
     *
     *    where A.T means `transpose(A)`
     */

using tensorflow::string;

namespace {

string DeviceName(const tensorflow::Device* d) {
  return (d == nullptr) ? "cpu:0" : d->name();
}

// Annotate eager runtime construction context to the given `function_def` as
// an attribute.
void AnnotateEagerRuntimeConstructionContext(
    tensorflow::FunctionDef& function_def) {
  tensorflow::AttrValue value;
  SetAttrValue("kEagerRuntime", &value);

    `tf.function(experimental_compile=True)` instead.
*   Adds `tf.function.experimental_get_compiler_ir` which returns compiler IR
    (currently 'hlo' and 'optimized_hlo') for given input for given function.

### Security:

*   Fixes an undefined behavior causing a segfault in `tf.raw_ops.Switch`,
    ([CVE-2020-15190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15190))

# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

# Verify that, given a build target and a license-list generator target, all of
# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets

                       no_reply=None,
                       bazel_config_name=None):
  """Set boolean action_env variable.

  Ask user if query_item will be enabled. Default is used if no input is given.
  Set environment variable and write to .bazelrc.

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".

//
// BackwardFunction needs to be a closure which stores intermediate activations
// from the forward computation and calls a vector-jacobian product function
// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//
// TODO(apassos) provide concrete template instantiations for TFE_TensorHandle
// specialization, which is blocked by quite a few things needing to loop back
// into python now.