}
      return r;
    }});

  render();

  // Helper functions follow:

  // hilite changes the highlighting of elements corresponding to specified src.
  function hilite(src, on) {
    if (on) {
      matches.add(src);
    } else {
      matches.delete(src);
    }
    toggleClass(src, 'hilite', on);
    return true;
  }

  // Display action menu (triggered by right-click on a frame)

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/op_stat_pass.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/stablehlo_util.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/transforms.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export_flags.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_translate_cl.h"
#include "tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/lstm_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/nms_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/perception_ops_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/tftext_utils.h"

func (x *Float) validate0() string {
	if x.form != finite {
		return ""
	}
	m := len(x.mant)
	if m == 0 {
		return "nonzero finite number with empty mantissa"
	}
	const msb = 1 << (_W - 1)
	if x.mant[m-1]&msb == 0 {
		return fmt.Sprintf("msb not set in last word %#x of %s", x.mant[m-1], x.Text('p', 0))
	}
	if x.prec == 0 {
		return "zero precision finite number"
	}
	return ""
}

      .Case("TANH", tflite::ActivationFunctionType_TANH)
      .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT);
}

static tflite::TensorType ConvertDerivedTFLiteTypeAttrForOptionWriter(
    tflite::TensorType type, flatbuffers::FlatBufferBuilder* builder) {
  if (type == tflite::TensorType_INT64) {
    return tflite::TensorType_INT64;
  } else if (type == tflite::TensorType_INT32) {

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h"

If you want to explore the SQLite database (file) directly, independently of FastAPI, to debug its contents, add tables, columns, records, modify data, etc. you can use <a href="https://sqlitebrowser.org/" class="external-link" target="_blank">DB Browser for SQLite</a>.

It will look like this:

<img src="/img/tutorial/sql-databases/image02.png">

bool IsStablehloOp(const tflite::OperatorCodeT &op_code);

// Returns the MLIR op name for the flatbuffer operator corresponding to
// `op_code`.
std::string GetMlirOpNameFromOpCode(const ::tflite::OperatorCodeT &op_code);

// Returns the builtin op code for the given MLIR operation on success; emits
// error and returns std::nullopt on failure.
std::optional<tflite::BuiltinOperator> GetBuiltinOpCode(Operation *mlir_op);

  for (auto dim : tensor.shape) {
    shape->add_dim()->set_size(int64_t{dim});
  }
  // TensorFlow Lite uses tflite::DynamicBufer to encode vector of strings.
  if (tensor.type == tflite::TensorType_STRING) {
    for (int i = 0; i < tflite::GetStringCount(buffer.data()); ++i) {
      tflite::StringRef str = tflite::GetString(buffer.data(), i);
      ret.add_string_val(str.str, str.len);
    }
    return ret;
  }