%0 = "tfl.pseudo_const"() {value = dense<0.0> : tensor<40xf32>} : () -> tensor<40xf32>
  %1 = "tfl.fully_connected"(%arg0, %arg1, %0) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> tensor<1x40xf32>
// CHECK: "tfl.fully_connected"
// CHECK-SAME: (tensor<1x37xf32>, tensor<40x37xf32>, none) -> tensor<1x40xf32>
  func.return %1 : tensor<1x40xf32>

// is quantized.
bool IsHybridQuantizedOp(Operation* op);

// Returns whether a given `stablehlo.dot_general` can be legalizable to
// `tfl.fully_connected`.
absl::StatusOr<bool> IsDotGeneralFullyConnected(
    ::mlir::stablehlo::DotGeneralOp dot_general_op);

// Returns the quantization dimension for a given `stablehlo.dot_general` op,

}

// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
table FullyConnectedOptions {
  // Parameters for FullyConnected version 1 or above.
  fused_activation_function:ActivationFunctionType;

  // Parameters for FullyConnected version 2 or above.
  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;

  // Parameters for FullyConnected version 5 or above.

  rewriter.replaceOp(slice_op, output_reshape_op.getResult());

  return success();
}

// ================== fully_connected ========================

// TFL fully_connected basically does:
// Weight * Input + bias.
// Input layout is : [..., depth]
// Weight layout is : [output, depth]
// Bias is [output].
//
// While conv2d is:
// Filter: [NHWC]

// CHECK-NOT: "tfl.reshape"
// CHECK: "tfl.slice"

// -----

func.func @fullyConnectedToConv(%arg0: tensor<384x384xf32>, %arg1: tensor<512x384xf32>, %arg2: tensor<512xf32>) -> tensor<384x512xf32> {
  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<384x384xf32>, tensor<512x384xf32>, tensor<512xf32>) -> tensor<384x512xf32>
  func.return %0: tensor<384x512xf32>

// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
table FullyConnectedOptions {
  // Parameters for FullyConnected version 1 or above.
  fused_activation_function:ActivationFunctionType;

  // Parameters for FullyConnected version 2 or above.
  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;

  // Parameters for FullyConnected version 5 or above.

    %8 = "tfl.concatenation"(%7, %arg5) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<4x3xf32>, tensor<4x2xf32>) -> tensor<4x5xf32>
    %9 = "tfl.fully_connected"(%8, %cst_10, %cst_0) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<4x5xf32>, tensor<8x5xf32>, tensor<8xf32>) -> tensor<4x8xf32>

        // input activations need to be asymmetrically quantized. Refer to the
        // implementation for fully-connected as an example in
        // tensorflow/lite/kernels/fully_connected.cc. The kernels will handle
        // the asymmetric_quantize_inputs attribute in the builtin option.
        dynamic_range_quantized_user->setAttr(
            kAsymmetricQuantizeInputsAttr,

  %4 = "tfl.dequantize"(%3) : (tensor<32x12x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x12xf32>
  %5 = "tfl.fully_connected"(%2, %4, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<32x12xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

      return dequantize.getInput();
    } else {
      return value;
    }
  };

  // Returns true if the TF::BatchMatMul operation can be converted to
  // tfl.fully_connected.
  auto can_convert_to_fully_connected =
      [&](BatchMatMulOpType& batch_matmul_op) {
        Value input_rhs = get_real_input_value(batch_matmul_op.getY());

        DenseElementsAttr constant;