GraphDef gdef;
  GetGraphDef(host_graph_, &gdef);
  std::vector<std::pair<string, string>> grads = GetGradDefs(gdef);
  ASSERT_EQ(2, grads.size());
  ASSERT_EQ("FooFunc1", grads[0].first);
  ASSERT_EQ("MyGrad", grads[0].second);
  ASSERT_EQ("FooFunc2", grads[1].first);
  ASSERT_EQ("MyGrad", grads[1].second);

  TF_DeleteFunction(func1);
  TF_DeleteFunction(func2);
  TF_DeleteFunction(grad_func);

    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

// Adds a copy of function `func` and optionally its gradient function `grad`
// to `g`. Once `func`/`grad` is added to `g`, it can be called by creating
// an operation using the function's name.
// Any changes to `func`/`grad` (including deleting it) done after this method
// returns, won't affect the copy of `func`/`grad` in `g`.
// If `func` or `grad` are already in `g`, TF_GraphCopyFunction has no

[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)

# SMB3 Advanced Features Implementation Plan

## Overview
This document outlines the implementation plan for advanced SMB3 features that are currently not implemented in JCIFS. These features are essential for enterprise-grade SMB3 support, providing enhanced performance, reliability, and scalability.

## Implementation Phases

### Phase 1: SMB3 Lease Implementation (Foundation)
**Priority: HIGH** | **Estimated Effort: 3-4 weeks**

MinIO [AIStor](https://www.min.io/product/aistor) includes enterprise-grade support and licensing for workloads which require commercial or proprietary usage and production-level SLA/SLO-backed support. For more information, [reach out for a quote](https://min.io/pricing).

## Source-Only Distribution

me see--how IS it to be managed?  I suppose I ought to eat or
drink something or other; but the great question is, what?'

  The great question certainly was, what?  Alice looked all round
her at the flowers and the blades of grass, but she did not see
anything that looked like the right thing to eat or drink under
the circumstances.  There was a large mushroom growing near her,
about the same height as herself; and when she had looked under

  TF_DeleteBuffer(t_buffer);
}

REGISTER_OP("TestOpWithNoGradient")
    .Input("x: T")
    .Output("y: T")
    .Attr("T: {float, double}")
    .Doc(R"doc(
Test op with no grad registered.

x: input
y: output
)doc")
    .SetShapeFn(tensorflow::shape_inference::UnknownShape);

class CApiGradientsTest : public ::testing::Test {
 protected:
  CApiGradientsTest()

### Instrumentation

In v1.9 we focused on improving stability of the components owned by the SIG, including Heapster, Custom Metrics API adapters for Prometheus, and Stackdriver.

### Network