namespace tensorflow {
namespace gradients {
Status NotDifferentiableGradientFunction::Compute(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs,
    absl::Span<AbstractTensorHandle*> grad_inputs) {
  for (int i = 0; i < grad_inputs.size(); i++) {
    grad_inputs[i] = nullptr;
  }
  return OkStatus();
}

Status RegisterNotDifferentiable(GradientRegistry* registry, const string& op) {

    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,
                          &numerical_grad_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,
//                  absl::Span<AbstractTensorHandle*> grad_outputs) override {
//     grad_outputs[0] = grad_inputs[0];
//     grad_outputs[1] = grad_inputs[0];
//     grad_outputs[0]->Ref();
//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//

#6c757d}.bg-gradient-gray .btn-tool,.bg-gray .btn-tool,.card-gray:not(.card-outline) .btn-tool{color:rgba(255,255,255,.8)}.bg-gradient-gray .btn-tool:hover,.bg-gray .btn-tool:hover,.card-gray:not(.card-outline) .btn-tool:hover{color:#fff}.card.bg-gradient-gray .bootstrap-datetimepicker-widget .table td,.card.bg-gradient-gray .bootstrap-datetimepicker-widget .table th,.card.bg-gray .bootstrap-datetimepicker-widget .table td,.card.bg-gray .bootstrap-datetimepicker-widget .table th{border:none}.card.bg-gradient-gray...

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  // Define the function and its grad
  DefineFunction(func_name_, &func_);
  TF_Function* grad_func;
  DefineFunction("MyGrad", &grad_func);

  // Add func and its gradient to host graph
  TF_GraphCopyFunction(host_graph_, func_, grad_func, s_);

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  TF_DeleteFunction(grad_func);

  const StackTracesMap* func_stack_traces;

                    <mxGeometry x="1110" y="410" width="190" height="500" as="geometry"/>
                </mxCell>
                <mxCell id="7" value="&lt;font style=&quot;font-size: 24px&quot; face=&quot;Roboto&quot;&gt;RAM&lt;br&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">

    deps = [
        "//tensorflow/c:env",
    ],
)

cc_library(
    name = "cleanup",
    hdrs = ["cleanup.h"],
)

cc_library(
    name = "ram_file_block_cache",
    srcs = ["ram_file_block_cache.cc"],
    hdrs = ["ram_file_block_cache.h"],
    deps = [
        ":cleanup",
        "//tensorflow/c:env",
        "//tensorflow/c:logging",
        "//tensorflow/c:tf_status",

#dee2e6;\n}\n\n.card-gray.card-outline-tabs > .card-header a.active {\n  border-top: 3px solid #6c757d;\n}\n\n.bg-gray .btn-tool,\n.bg-gradient-gray .btn-tool,\n.card-gray:not(.card-outline) .btn-tool {\n  color: rgba(255, 255, 255, 0.8);\n}\n\n.bg-gray .btn-tool:hover,\n.bg-gradient-gray .btn-tool:hover,\n.card-gray:not(.card-outline) .btn-tool:hover {\n  color: #ffffff;\n}\n\n.card.bg-gray .bootstrap-datetimepicker-widget .table td,\n.card.bg-gray .bootstrap-datetimepicker-widget .table th,\n.card.bg-gradient-gray...