def file3 = new File("artifact-3.zip")

        def conf1 = dependencyScope("conf1")
        def conf2 = dependencyScope("conf2")
        def child1 = consumable("child1", [conf1, conf2])
        consumable("child2", [conf1])

        addArtifact(conf1, artifact1, file1)
        addArtifact(conf2, artifact2, file2)
        addArtifact(child1, artifact3, file3)

        when:

  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x2x2xbf16>) -> tensor<1x3x2x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
  return %3 : tensor<1x3x2x2xf32>
// CHECK: tf.Conv2D
// CHECK-SAME: loc(callsite("Model/conv2d@conv2d_with_valid_loc"("Conv2D") at "QuantizationUnit({{.*}})"))
}

                conf.files
                configurations.remove(conf)

                def conf2 = configurations.create("conf2")
                conf2.dependencies.add(project.dependencies.create("org:foo:1.0"))
                conf2.files
            }
        """

        expect:
        succeeds("resolve")
    }

   // Unsupported data format
   %1 = "tf.Conv2D"(%arg2, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NCHW", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x3x32x32xf32>, tensor<3x3x3x16xf32>) -> tensor<256x16x32x32xf32>
   // OK

    %1 = "tf.Conv2D"(%0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true}
        : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32> loc(fused["Conv2D:", "Model/conv2d"])
    %2 = "tf.IdentityN"(%1) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>

    }
  }

  // Handle Conv2D input, stride and filter.
  HandleConv2DInput(conv2d, block_size);
  HandleConv2DStride(conv2d);
  HandleConv2DFilter(conv2d, block_size);

  // Book keeping new filter shape for backprop filter rewrite.
  // Filter shape is defined in HandleConv2DFilter, thus it is RankedTensorType.
  filter_shape =
      mlir::cast<RankedTensorType>(conv2d.getFilter().getType()).getShape();

        scale, offset = [1.0] * 2, [0.5] * 2
        mean, variance = scale, offset
        out = nn_ops.conv2d(
            q_input,
            q_filter,
            strides=[1, 1, 2, 1],
            dilations=[1, 1, 1, 1],
            padding='SAME',
            data_format='NHWC',
            name='sample/conv2d',
        )
        if has_bias:
          out = nn_ops.bias_add(out, bias, data_format='NHWC')

    return %3 : tensor<?x?x?x2xf32>
  }

// CHECK-LABEL: func @conv_with_dynamic_shape
// The Conv2D should not be quantized since it has dynamic channel.
// CHECK: "tf.Conv2D"
// CHECK-SAME: (tensor<?x?x?x?xf32>, tensor<2x3x3x2xf32>) -> tensor<?x?x?x2xf32>

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: dilations = [1, 4, 2, 3]
  // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
  // CHECK-SAME: padding = "EXPLICIT"

  }
  func.func private @composite_conv2d_with_bias_and_relu6_fn_2(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {