Input translation, const string& kernel_type, bool antialias,
              Output* x, Output* y) {
    *x = Const<T>(scope_, x_data);
    *y = ScaleAndTranslate(scope_, *x, y_shape, scale, translation,
                           ScaleAndTranslate::KernelType(kernel_type)
                               .Antialias(antialias)
                               .Antialias(antialias));

                                   const std::vector<Output>& grad_inputs,
                                   std::vector<Output>* grad_outputs) {
  string kernel_type;
  TF_RETURN_IF_ERROR(
      GetNodeAttr(op.node()->attrs(), "kernel_type", &kernel_type));
  bool antialias;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "antialias", &antialias));
  grad_outputs->push_back(internal::ScaleAndTranslateGrad(

%arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15, %arg16, %arg17, %cst0, %cst1, %arg18, %arg19, %arg20, %arg21) ({}) {cell_clip = 0.000000e+00 : f32, fused_activation_function = "NONE", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.000000e+00 : f32} : (tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>,...

// CHECK-LABEL: @main

// CHECK-NEXT:      builtin_options: {
// CHECK-NEXT:        fused_activation_function: RELU,
// CHECK-NEXT:        cell_clip: 1.0,
// CHECK-NEXT:        proj_clip: 2.0,
// CHECK-NEXT:        kernel_type: BASIC
// CHECK-NEXT:      },
// CHECK-NEXT:      intermediates: [ ]
// CHECK-NEXT:    } ],
// CHECK-NEXT:    name: "main"
// CHECK-NEXT:  } ],
// CHECK-NEXT:  description: "MLIR Converted.",

  mlir::TFL::LSTMKernelType kernel_type;
  switch (value) {
    case tflite::LSTMKernelType_FULL:
      kernel_type = mlir::TFL::LSTMKernelType::FULL;
      break;
    case tflite::LSTMKernelType_BASIC:
      kernel_type = mlir::TFL::LSTMKernelType::BASIC;
      break;
  }
  return mlir::TFL::LSTMKernelTypeAttr::get(builder.getContext(), kernel_type);
}

%arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15, %arg16, %arg17, %cst0, %cst1, %arg18, %arg19, %arg20, %arg21) ({}) {cell_clip = 0.000000e+00 : f32, fused_activation_function = "NONE", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.000000e+00 : f32} : (tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>,...

%arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15, %arg16, %arg17, %cst0, %cst1, %arg18, %arg19, %arg20, %arg21) ({}) {asymmetric_quantize_inputs = true, cell_clip = 0.000000e+00 : f32, fused_activation_function = "NONE", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.000000e+00 : f32} : (tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>,...

  %0 = "tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15, %arg16, %arg17, %arg18, %arg19, %arg20, %arg21, %arg22, %arg23) ({}) {cell_clip = 1.000000e+00 : f32, fused_activation_function = "NONE", kernel_type = #tfl<lstm_kernel_type_attr BASIC>} : (tensor<?xf32>,...

tensor<0x!quant.uniform<i16:f32, 0.0087630003690719604>>, input_to_output_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0057529998011887074>>, effective_hidden_scale_intermediate = tensor<0x!quant.uniform<i8<-127:127>:f32, 0.0075630000792443752:2>>, kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.01 : f32} : (tensor<1x528x!quant.uniform<i8:f32, 0.037248000502586365:-19>>, tensor<2048x528x!quant.uniform<i8<-127:127>:f32, 0.059801999479532242>>, tensor<2048x528x!quant.uniform<i8<-127:127>:f32,...