ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,

  ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,

  // CHECK: %[[NONE:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK: "tfl.transpose_conv"(%arg0, %arg1, %arg2, %[[NONE]])
  %cst = "tfl.no_value"() {value = unit} : () -> none

}

int64_t TransposeConvOp::GetArithmeticCount(Operation* op) {
  int64_t count = -1;
  auto transpose_conv = llvm::dyn_cast<TransposeConvOp>(op);
  auto input_type = transpose_conv.getInput()
                        .getType()
                        .dyn_cast_or_null<mlir::RankedTensorType>();
  auto weight_type = transpose_conv.getWeights()
                         .getType()

  // CHECK: %[[ARG1:.*]] = "tfl.transpose_conv"(%arg0, %[[ARG0]], %arg2, %[[CST_0]]) <{fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<4xi32>, tensor<1x3x3x32xf32>, tensor<15x14x14x32xf32>, none) -> tensor<15x28x28x1xf32>

          /*depthwise_multiplier=*/rewriter.getI32IntegerAttr(multiplier));
    } else if (is_transpose_conv) {
      // TODO: b/326332748 - For forward convolution in transpose_conv,
      // IsSamePadding calculation may need to be updated.
      // Reference: https://arxiv.org/pdf/1603.07285.pdf
      // Section 4.6 > Relationship 13 states `stride_dim = dilation + 1`.

    }
    return success();
  };

 private:
  // Utility function to check if the padding on the mhlo.convolution op
  // equals to the padding needed on a transpose_conv for the same
  // inputs/outputs, to achieve the effect of VALID padding.
  bool IsValidPaddingForTransposedConv(mhlo::ConvolutionOp conv_op,
                                       size_t num_spatial_dims,

// CHECK: %[[PAD:.+]] = "tfl.pad"(%[[ARG_0]], %[[CONST_1]]) : (tensor<1x5x5x2x!quant.uniform<i8:f32, 2.000000e+00>>, tensor<4x2xi32>) -> tensor<1x7x7x2x!quant.uniform<i8:f32, 2.000000e+00>>

  );

  let results = (outs
    TFL_BoolTensor:$output
  );

  let hasOptions = 1;
  let customOption = "ReducerOptions";
}

def TFL_TransposeConvOp: TFL_Op<"transpose_conv", [
    Pure,
    TFL_OperandHasRank<0, 1>,
    TFL_OperandHasRank<1, 4>,
    TFL_OperandHasRank<2, 4>,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 2>>,

    type = mlir::cast<ShapedType>(cst.getType());
  } else {
    result.can_compress = false;
    return result;
  }

  // Currently we only support compressing weights of ops:
  //   Conv, DepthwiseConv, TransposeConv, whose filter has rank 4, and
  //   FullyConnected, whose filter has rank 2.
  if (type.getRank() != 2 && type.getRank() != 4) {
    result.can_compress = false;
    return result;
  }