{`\"`, `"`},
		{`\'`, `'`},
		{`\.`, `.`},
		{`\. .`, `. .`},
		{
			`The \3c i\3equick\3c/i\3e,\d\A\3cspan style=\27 color:brown\27\3e brown\3c/span\3e  fox jumps\2028over the \3c canine class=\22lazy\22 \3e dog\3c/canine\3e`,
			"The <i>quick</i>,\r\n<span style='color:brown'>brown</span> fox jumps\u2028over the <canine class=\"lazy\">dog</canine>",
		},
	}
	for _, test := range tests {
		got1 := string(decodeCSS([]byte(test.css)))

	}
	globalCtx, globalSpan := Start(ctx, rootSpan)
	return globalCtx, func() {
		globalSpan.End()
		shutdown()
	}, nil
}

func Start(ctx context.Context, span string) (context.Context, traceapi.Span) {
	return tracer().Start(ctx, span)

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++++

Everyone has to start somewhere, and if you're new to Gradle, this is where to begin.

To get started using Gradle:

apiVersion: release-notes/v2
kind: feature
area: telemetry
issue:
  - 48336
releaseNotes:
  - |

==============================================================================*/

#include "tensorflow/c/experimental/saved_model/core/revived_types/flat_tensor_function.h"

#include <memory>
#include <string>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/immediate_execution_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"

#include "tensorflow/c/experimental/gradients/not_differentiable.h"

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 absl::OkStatus();
}

  if (attr_.full_type() == "list(string)") {
    return "absl::Span<string const>";
  }

  // Normal path: translate base type to C++ ...
  string c_base_type = attr_.base_type();
  if (attr_.base_type() == "type") {
    c_base_type = "DataType";
  } else if (attr_.base_type() == "shape") {
    c_base_type = "const PartialTensorShape";
  }

  // ... and wrap in a Span<> if it's a list.
  if (attr_.is_list()) {

  }
};

class PassThroughGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);
    grad_inputs[0] = grad_outputs[0];
    if (grad_inputs[0]) {

string ArgTypeView::TypeName() const {
  if (arg_type_.is_read_only()) {
    if (arg_type_.is_list()) {
      return "absl::Span<AbstractTensorHandle* const>";
    } else {
      return "AbstractTensorHandle* const";
    }
  } else {
    if (arg_type_.is_list()) {
      return "absl::Span<AbstractTensorHandle*>";
    } else {
      return "AbstractTensorHandle**";
    }
  }
}

}  // namespace cpp

#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_TENSORFLOW_CALIBRATOR_CALIBRATION_STATISTICS_COLLECTOR_BASE_H_

#include <cstdint>
#include <optional>

#include "absl/types/span.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.pb.h"

namespace tensorflow {
namespace calibrator {

// Abstract base class for CalibrationStatisticsCollcetor such as