func.return %0 : tensor<8x4x16x1xf32>
}

}

func.func @einsum_reshapetail(%arg0: tensor<3x4x5xf32>, %arg1: tensor<5x6x2xf32>) -> tensor<3x4x6x2xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "bfd,dnh->bfnh"}: (tensor<3x4x5xf32>, tensor<5x6x2xf32>) -> tensor<3x4x6x2xf32>
  func.return %0 : tensor<3x4x6x2xf32>
  // CHECK-LABEL: einsum_reshapetail
  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<[5, 12]> : tensor<2xi64>

    rhs_quantization_min_val = -128 : i64,
    rhs_quantization_max_val = 127 : i64
  } : (tensor<1x6x6x3xf32>, tensor<2x3x3x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1x4x1x2xf32>
  func.return %0 : tensor<1x4x1x2xf32>
}

// -----

// CHECK-LABEL: func @uniform_quantized_convolution_hybrid_same
func.func @uniform_quantized_convolution_hybrid_same(%input: tensor<1x2x2x3xf32>) -> tensor<1x2x1x2xf32> {

    func.return %0 : tensor<?x?x?x?x?xf32>
  }

  // CHECK-LABEL: testXlaConvV2OpInconsistentInputAndKernelTensorShapeDim
  func.func @testXlaConvV2OpInconsistentInputAndKernelTensorShapeDim(%lhs: tensor<8x4x16x16xf32>, %rhs: tensor<3x4x4x16x16xf32>) -> (tensor<?x?x?x?x?xf32>) {
    %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>