break;
      case COSH:
        y = Cosh(scope_, x);
        break;
      case TANH:
        y = Tanh(scope_, x);
        break;
      case ASINH:
        y = Asinh(scope_, x);
        break;
      case ACOSH:
        y = Acosh(scope_, x);
        break;
      case ATANH:
        y = Atanh(scope_, x);
        break;
      case SIGMOID:
        y = Sigmoid(scope_, x);
        break;

    %9, %9, %9, %9) {
      asymmetric_quantize_inputs = false,
      cell_clip = 1.000000e+01 : f32,
      effective_hidden_scale_intermediate = tensor<0x!quant.calibrated<f32<0.0:1.0>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<0xf32>,
      input_to_forget_intermediate = tensor<0xf32>,
      input_to_input_intermediate = tensor<0xf32>,
      input_to_output_intermediate = tensor<0xf32>,

"tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %cst, %cst, %cst, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg19, %arg20, %arg15, %arg16, %arg17, %arg18) ({}) {cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", input_to_input_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0049890000373125076>>, input_to_forget_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0078849997371435165>>, input_to_cell_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0087630003690719604>>,...

    // Populate attributes.
    SmallVector<NamedAttribute, 4> attributes;
    // Activation will always be tanh.
    attributes.push_back(rewriter.getNamedAttr("fused_activation_function",
                                               rewriter.getStringAttr("TANH")));
    // cell_clip.
    attributes.push_back(
        rewriter.getNamedAttr("cell_clip", rewriter.getF32FloatAttr(0.0)));
    // proj_clip.

    %4 = mhlo.exponential %3 : tensor<13x13x13xf32>
    %5 = mhlo.tanh %3 : tensor<13x13x13xf32>
    return %4, %5 : tensor<13x13x13xf32>, tensor<13x13x13xf32>

// CHECK:      "mhlo.pad"(%arg0, %0) <{
// CHECK-SAME:     edge_padding_high = dense<2> : tensor<3xi64>,

  // inconsistent with Conv 1x1 which always performs per channel quantization.
  bool disable_per_channel_for_dense_layers = false;

  // Whether to use fixed output ranges of the activation ops (tanh, sigmoid,
  // etc.) and not infer weight constants.
  // If this option is set, quantization emulation ops should be placed after
  // the ops in the input graph. This flag should be set to false for

      input_cell_state_, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_, builder_.getStringAttr("TANH"),
      builder_.getF32FloatAttr(10.0), builder_.getF32FloatAttr(0.0),
      mlir::TFL::LSTMKernelTypeAttr::get(builder_.getContext(),
                                         mlir::TFL::LSTMKernelType::FULL),

//CHECK-NEXT: return %0 : tensor<1x30x1xi32>
//CHECK-NEXT:}

func.func @tanh(%arg0: tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32> {
 %0 = "vhlo.tanh_v1" (%arg0) : (tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32>
 func.return %0 : tensor<1x1x1x96xf32>
}

//CHECK:func.func private @tanh(%arg0: tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32> {

  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,
  PAD = 34,
  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
  GATHER = 36,
  BATCH_TO_SPACE_ND = 37,

      %cst_2, %cst_2, %cst_2,
      %cst_7, %cst_7, %cst_7, %cst_7,
      %cst_2, %cst_2,
      %cst_1, %cell_input,
      %cst_2, %cst_2, %cst_2, %cst_2) {cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", proj_clip = 0.000000e+00 : f32, time_major = false}
    :  ( tensor<1x28x28x!quant.uniform<i8:f32, 0.003:-128>>,