// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32>
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

   // 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

  // CHECK: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL1]]) : (tensor<1x1x1x8x16x!quant.uniform<{{.+}}>>) -> tensor<1x1x1x8x16xf32>
  // CHECK: %[[VAL3:.+]] = "tfl.conv_3d"(%arg0, %[[VAL2]], %[[VAL0]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}>

module {
func.func @main(%arg0: tensor<4x68x68x3xf32>, %arg1: tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32>
  func.return %0 : tensor<4x64x64x8xf32>
}

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 4
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class
        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):

  %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({{.*}})"))
}

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 4
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {