%1 = "tf.ExpandDims"(%0, %cst_0) : (tensor<2x68x3xf32>, tensor<i32>) -> tensor<2x1x68x3xf32>
  %2 = "tf.Conv2D"(%1, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<2x1x68x3xf32>, tensor<1x5x3x8xf32>) -> tensor<2x1x64x8xf32>
  %3 = "tf.Squeeze"(%2) {squeeze_dims = [-3]} : (tensor<2x1x64x8xf32>) -> tensor<2x64x8xf32>

    %15 = "quantfork.qcast"(%14) {volatile} : (tensor<2x3x3xf32>) -> tensor<2x3x3x!quant.uniform<i8:f32, 3.000000e-01:1>>
    %16 = "quantfork.dcast"(%15) : (tensor<2x3x3x!quant.uniform<i8:f32, 3.000000e-01:1>>) -> tensor<2x3x3xf32>
    return %16 : tensor<2x3x3xf32>
  }

  // CHECK: quantized_dot_general_fn_1

    value {
      b: false
    }
  }
  attr {
    key: "adj_y"
    value {
      b: false
    }
  }
}
versions {
  producer: 175
}

# CHECK:       func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {

}
versions {
  producer: 175
}

# CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {
# CHECK:     %[[VAL_2:.*]] = "tfl.batch_matmul"(%[[VAL_0]], %[[VAL_1]]) <{adj_x = false, adj_y = false}> : (tensor<2x5x3xf32>, tensor<3x7xf32>) -> tensor<2x5x7xf32>
# CHECK:     return %[[VAL_2]] : tensor<2x5x7xf32>

  // CHECK: return %[[v2]] : tensor<2x5x3xf32>
}

func.func @einsum_transposereduceddim(%arg0: tensor<2x5x7xf32>, %arg1: tensor<2x5x3x7xf32>) -> tensor<2x5x3xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "bij,binj->bin"}: (tensor<2x5x7xf32>, tensor<2x5x3x7xf32>) -> tensor<2x5x3xf32>
  func.return %0 : tensor<2x5x3xf32>
  // CHECK-LABEL: einsum_transposereduceddim

    %2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<2x2x3xf32>) -> tensor<2x2x3xf32>
    return %2 : tensor<2x2x3xf32>
  }
// CHECK: func.func private @quantize_dot_general_batch_per_tensor_quantized_fn(%[[ARG_0:.+]]: tensor<2x2x2xf32>) -> tensor<2x2x3xf32> attributes {tf._original_func_name = "main_0"}

  // CHECK:  return
}

func.func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>

  // CHECK-LABEL: pow
  // CHECK:  %[[pow:.*]] = tfl.pow(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  // CHECK:  return
}

func.func @testPadUnknownPaddings(tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<*xi32>):
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>

  // CHECK-LABEL: testPadUnknownPaddings
  // CHECK:  "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<?xf32>
  // CHECK:  return
}

// -----

    %12 = "tf.Mul"(%11, %cst) {device = ""} : (tensor<2x1x3xf32>, tensor<f32>) -> tensor<2x1x3xf32>
    %13 = "tf.Identity"(%12) {device = ""} : (tensor<2x1x3xf32>) -> tensor<2x1x3xf32>
    %14 = "tf.Identity"(%13) {device = ""} : (tensor<2x1x3xf32>) -> tensor<2x1x3xf32>
    return %14 : tensor<2x1x3xf32>
  }

// CHECK-LABEL: func @broadcasting_weight_batch_matmul

func.func @QuantizePad2(tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, tensor<2x1x3xf32>, tensor<3x2xi32>) -> (tensor<?xf32>, tensor<?xf32>) {
^bb0(%arg0: tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<2x1x3xf32>, %arg2: tensor<3x2xi32>):
  %0 = "tfl.dequantize"(%arg0) : (tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>) -> tensor<2x1x3xf32>
  %1 = "tfl.pad"(%arg1, %arg2) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32>