],
)

tf_cc_test(
    name = "framework_gradients_test",
    srcs = ["framework/gradients_test.cc"],
    deps = [
        ":cc_ops",
        ":client_session",
        ":grad_op_registry",
        ":grad_ops",
        ":gradients",
        ":testutil",
        "//tensorflow/core:all_kernels",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:protos_all_cc",

                     llvm::SmallVectorImpl<Operation*>& erase_list,
                     llvm::SmallPtrSetImpl<Operation*>& dead_ops) {
  for (mlir::Value res : op->getResults()) {
    for (Operation* user : res.getUsers()) {
      if (!dead_ops.insert(user).second) continue;
      RecursiveRemove(user, erase_list, dead_ops);
    }
  }

  erase_list.push_back(op);

  for (auto& use : op->getOpOperands()) {

def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res

  shape = input_.shape.as_list()
  for i in range(len(shape)):
    rdims = tf.raw_ops.OneHot(
        indices=i, depth=len(shape), on_value=True, off_value=False, axis=-1)
    rarray = tf.raw_ops.Reverse(tensor=input_, dims=rdims)

    left_padding_size = tf.raw_ops.GatherNd(params=paddings, indices=[i, 0])
    right_padding_size = tf.raw_ops.GatherNd(params=paddings, indices=[i, 1])

    if mode == 'REFLECT':

    ],
    licenses = ["notice"],
)

package_group(
    name = "friends",
    packages = [
        "//tensorflow/compiler/mlir/tfr/...",
    ],
)

gen_op_libraries(
    name = "pad_ops",
    src = "ops_defs.py",
    deps = [
        "//tensorflow:tensorflow_py",
        "//tensorflow/python/ops:array_ops_gen",
        "//tensorflow/python/platform:flags",
        "@absl_py//absl:app",
    ],
)

def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res

```

    }
    kwargs_ = {
        'input_': input_,
        'paddings': paddings,
        'mode': mode,
    }
    # Make sure the composition python function is correct
    self._assertOpAndComposite([input_], tf.raw_ops.MirrorPad,
                               ops_defs._composite_mirror_pad, kwargs_, kwargs)
    # Make sure the translation and decomposition is correct
    self._assertOpAndComposite([input_],

  // The "truth" labels are at index 1.
  auto softmax_grad = op.output(1);

  // The loss is the output at index 0, and backprop is the output at index 1.
  auto grad_loss = grad_inputs[0];
  auto grad_grad = grad_inputs[1];

  auto grad = BroadcastMul(scope, grad_loss, softmax_grad);
  if (!IsZero(scope, grad_grad)) {
    std::vector<int> axis;
    auto logits_softmax = Softmax(scope, logits);

*   Fixes a security vulnerability caused by lack of validation in
    `tf.raw_ops.DataFormatVecPermute` and `tf.raw_ops.DataFormatDimMap`
    ([CVE-2020-26267](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26267))
*   Fixes a vulnerability caused by attempting to write to immutable memory
    region in `tf.raw_ops.ImmutableConst`
    ([CVE-2020-26268](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26268)

  int axis;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "axis", &axis));

  grad_outputs->reserve(N);
  auto grad_op = Unstack(scope, grad_inputs[0], N, Unstack::Axis(axis));
  for (const Output& o : grad_op.output) {
    grad_outputs->emplace_back(o);
  }
  return scope.status();
}
REGISTER_GRADIENT_OP("Pack", PackGrad);