func (dt *transportDialTester) wantDial() *transportDialTesterConn {
	c := <-dt.dials
	c.connID = dt.dialCount
	dt.dialCount++
	dt.t.Logf("Dial %v: started", c.connID)
	return c
}

// finish completes a Dial.
func (c *transportDialTesterConn) finish(err error) {
	c.t.Logf("Dial %v: finished (err:%v)", c.connID, err)
	c.ready <- err
	close(c.ready)

  %conv = "tfl.conv_2d"(%0, %w, %b) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {

// CHECK-NEXT: %[[conv:.*]] = "tfl.conv_2d"(%[[q]], %[[cst1]], %[[cst2]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>,...

func.func @not_lift_float_conv(%arg0: tensor<1x3x4x512xf32>) -> (tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x512x512xf32>} : () -> tensor<2x3x512x512xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],
    padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true

  %cst = arith.constant dense<1.5> : tensor<f16>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf16>

parts of the aggregate.

  6. Conveying Non-Source Forms.

  You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:

    a) Convey the object code in, or embodied in, a physical product
    (including a physical distribution medium), accompanied by the

    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@conv_net_2d/conv_2d_0/w"
      }
    }
  }
}
node {
  name: "conv_net_2d_1/conv_2d_0/convolution"
  op: "Conv2D"
  input: "input"
  input: "conv_net_2d/conv_2d_0/w/read"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"
    value {
      s: "NCHW"
    }

  %6 = "tf.Cast"(%5) {Truncate = false} : (tensor<1x100x100x1xi8>) -> tensor<1x100x100x1xf32>

  %7 = "tf.Sub"(%6, %cst_4) : (tensor<1x100x100x1xf32>, tensor<f32>) -> tensor<1x100x100x1xf32>
  %8 = "tf.Conv2D"(%7, %cst_3) {dilations = [1, 1, 1, 1], padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<1x100x100x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x98x98x1xf32>

    %5 = "tf.MatMul"(%1, %3) {
      attr_map = "transpose_a:0,transpose_b:1"
    } : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    func.return %5 : tensor<*xi32>
  }

  // Conv2D with int32 accumulation
  func.func private @internal_conv2d_fn(
                         %input : tensor<*xi8>, %filter : tensor<*xi8>,
                         %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,

    %conv = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 2, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
    return %conv : tensor<*xf32>
  }