// CHECK-DAG: %[[INPUT_QINT:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1x2x2x1xi32>) -> tensor<1x2x2x1x!tf_type.qint32>
  %q_input = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x2x2x1xi32>) -> tensor<1x2x2x1x!tf_type.qint32>

  // CHECK-DAG: %[[MIN_QINT:.*]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<i32>) -> tensor<!tf_type.qint32>

  %1 = "tfr.cast"(%arg0) : (!tfr.tensor) -> tensor<1x4x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>
  %2 = "tfr.cast"(%arg1) : (!tfr.tensor) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>
  %3 = "tfr.cast"(%2) : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>) -> !tfr.tensor

// CHECK-DAG: %[[scale_factor:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[cast:.*]] = "tfr.cast"(%[[scale_factor]]) : (tensor<f32>) -> !tfr.tensor
// CHECK: %[[cast_perchannel:.*]] = "tfr.cast"(%[[scale_factors]]) : (tensor<2xf32>) -> !tfr.tensor
// CHECK: return %[[cast]], %[[cast_perchannel]] : !tfr.tensor, !tfr.tensor
}

// CHECK-LABEL: decompose_quant_scale_factor_invalid

    %float_in_zp = "tf.Cast"(%input_zp) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32>
    %float_out_zp = "tf.Cast"(%out_zp) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32>

    %cast = "tf.Cast"(%input) {Truncate = false} : (tensor<*xi8>) -> tensor<*xf32>
    %sub = "tf.Sub"(%cast, %float_in_zp) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
    %input_i32 = "tf.Cast"(%identity) : (tensor<*xi8>) -> tensor<*xi32>
    %output = "tf.Sub"(%input_i32, %zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    %cast = "tf.Cast"(%output) : (tensor<*xi32>) -> tensor<*xf32>
    %mul = "tf.Mul"(%cast, %scale) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %mul : tensor<*xf32>
  }

    if (!mlir::cast<ShapedType>(lhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(rhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(cond.getType()).hasStaticShape()) {
      return rewriteOpWithDynamicInput(op, rewriter);
    }

    auto lhs_shape = mlir::cast<ShapedType>(lhs.getType()).getShape();
    auto rhs_shape = mlir::cast<ShapedType>(rhs.getType()).getShape();

    // Check if the same-scale requirement is met.
    auto dq_arg_type = preceding_sc_op.getArg().getType().cast<TensorType>();
    auto qtype = dq_arg_type.getElementType().cast<QuantizedType>();
    auto q_result_type = sc_op.getType().cast<TensorType>();
    auto out_qtype = q_result_type.getElementType().cast<QuantizedType>();
    if (qtype != out_qtype) {
      avg_pool_op.emitError(

            MapProperty<Object, Object> asMapProperty = Cast.uncheckedNonnullCast(target);
            DefaultProvider<Map<Object, Object>> convention = new DefaultProvider<>(() -> Cast.uncheckedNonnullCast(mapping.getValue(_convention, _source)));
            asMapProperty.convention(convention);
        } else if (target instanceof HasMultipleValues) {
            HasMultipleValues<Object> asCollectionProperty = Cast.uncheckedNonnullCast(target);

  transpose_op++;
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getOperand(0).getType())
                .getDimSize(0),
            3);
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getOperand(0).getType())
                .getDimSize(1),
            12);
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getResult(0).getType())
                .getDimSize(0),

        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>
        %cast = "tf.Cast"(%output) : (tensor<1024x24x24x3xi32>) -> tensor<1024x24x24x3xf32>
        %mul = "tf.Mul"(%cast, %scale) : (tensor<1024x24x24x3xf32>, tensor<f32>) -> tensor<1024x24x24x3xf32>