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());

[**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

  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()

*/\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb...

2F8A          ; mapped                 ; 8272          # 3.0  KANGXI RADICAL COLOR
2F8B          ; mapped                 ; 8278          # 3.0  KANGXI RADICAL GRASS
2F8C          ; mapped                 ; 864D          # 3.0  KANGXI RADICAL TIGER
2F8D          ; mapped                 ; 866B          # 3.0  KANGXI RADICAL INSECT