],
)

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

    ],
    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",
    ],
)

*   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);

    // safe.
    if (auto tensor_array_v3_op = dyn_cast<TF::TensorArrayV3Op>(tf_op)) {
      // The "flow" in TensorArrayV3 is always a scalar float tensor.
      // https://www.tensorflow.org/api_docs/python/tf/raw_ops/TensorArrayWriteV3
      Value flow = tensor_array_v3_op.getFlow();
      Type scalar_f32_tensor_type =
          RankedTensorType::get(/*shape=*/{}, rewriter.getF32Type());
      flow.setType(scalar_f32_tensor_type);

  // possible terminator.
  return std::next(block->begin()) == block->end() ||
         std::next(block->begin(), 2) == block->end();
}

// This pattern matches GraphOps with only one FetchOp (empty) and remaps the
// results of the GraphOp to the operands of the FetchOp.
struct DropEmptyGraph : public OpRewritePattern<GraphOp> {
  using OpRewritePattern<GraphOp>::OpRewritePattern;

  def model_fn(args):
    # do something with dynamic tensor

  @function.Defun(capture_resource_var_by_value=False)
  def tpu_subgraph():
      return tf.tpu.rewrite(model_fn, args)

  return tf.raw_ops.TPUPartitionedCall(
      args=tpu_subgraph.captured_inputs,
      Tout=[o.type for o in tpu_subgraph.definition.signature.output_arg],
      f=tpu_subgraph,
      device_ordinal=[0])

elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
value is computed as \\( \sqrt{a^2 + b^2}\\).

For example:

>>> x = tf.complex(3.0, 4.0)
>>> print((tf.raw_ops.ComplexAbs(x=x, Tout=tf.dtypes.float32, name=None)).numpy())
5.0
  }];

  let arguments = (ins
    TensorOf<[TF_Complex128, TF_Complex64]>:$x
  );

  let results = (outs