for (auto input_type : func_type.getInputs()) {
    tensorflow::TensorShape tensor_shape;
    xla::Shape xla_shape = xla::TypeToShape(input_type);
    TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape(
        xla_shape.dimensions(), &tensor_shape));
    arg_shapes.emplace_back(tensor_shape);

    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));

    int quant_dim = -1;
    auto input_type = mlir::cast<ShapedType>(input.getType());
    if (PerAxis) {
      if (!input_type.hasRank()) {
        tf_op.emitError("The input should have known rank for per-channel op.");
        return failure();
      }
      // This is a special case that the quant_dim is the last dimensions.
      quant_dim = input_type.getRank() - 1;
    }

  output_shape[1] = composite_result_shape[2];
  output_shape[2] = composite_result_shape[3];
  output_shape[3] = composite_result_shape[1];

  auto input_type = mlir::cast<ShapedType>(old_op->getOperand(0).getType());

  return RankedTensorType::get(output_shape, input_type.getElementType());
}
}  // namespace odml

      OP_REQUIRES(context, context->input_type(i * 3) == DT_FLOAT,
                  absl::AbortedError("The input `min` must have float type."));
      OP_REQUIRES(context, context->input_type(i * 3 + 1) == DT_FLOAT,
                  absl::AbortedError("The input `max` must have float type."));
      OP_REQUIRES(
          context, context->input_type(i * 3 + 2) == DT_INT64,

// Quantizes the input model represented as `model_buffer` and writes the result
// to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a
// valid FlatBuffer format for Model supported by TFLite.
//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//

               << ", input_inference_type: "
               << tflite::EnumNameTensorType(input_type)
               << ", output_inference_type: "
               << tflite::EnumNameTensorType(output_type) << "\n";
  mlir::Builder mlir_builder(&context);
  mlir::Type input_mlir_type =
      tflite::ConvertElementType(input_type, mlir_builder);
  mlir::Type output_mlir_type =

    if (!reshape_type.hasStaticShape()) return failure();
    ArrayRef<int64_t> reshape_shape = reshape_type.getShape();

    auto input_type = mlir::cast<ShapedType>(op.getInput().getType());
    auto output_type = mlir::cast<ShapedType>(op.getOutput().getType());

    if (!input_type.hasRank() || !output_type.hasRank()) return failure();

    // The pattern attempts to reduce the rank of the input to BroadcastTo.

    auto qbarrier = rewriter.create<QuantizeCastOp>(op.getLoc(), quantizedType,
                                                    op.getInputs());
    rewriter.replaceOpWithNewOp<DequantizeCastOp>(op, converter.input_type,
                                                  qbarrier.getResult());

    return false;
  }
};

class ConstFakeQuantRewrite
    : public FakeQuantRewrite<ConstFakeQuantRewrite, ConstFakeQuant> {

    for (int index : dynamic_shape_arg_index) {
      BlockArgument arg = func.getArgument(index);
      auto inputType = mlir::dyn_cast<RankedTensorType>(arg.getType());
      // Only rank 1 tensor is supported for now.
      if (!inputType || inputType.getRank() != 1) continue;
      auto shape = llvm::to_vector<4>(inputType.getShape());
      llvm::SmallVector<int64_t, 4> bounds(shape.begin(), shape.end());
      // Mark the dim as dynamic dim.

        File input = getInput().get().getAsFile();
        InstrumentationInputType inputType = getInputType(input);
        switch (inputType) {
            case DEPENDENCY_ANALYSIS_DATA:
                doOutputTransformedFile(input, outputs);
                return;
            case ORIGINAL_ARTIFACT: