// CHECK-NEXT:   }, {
// CHECK-NEXT:   name: "model_control_dependencies",
// CHECK-NEXT:   buffer: 7
// CHECK-NEXT:   } ],
// CHECK-NEXT:   signature_defs: [ ]
// CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")

  // CHECK-NEXT:   metadata: [ {
  // CHECK-NEXT:   name: "min_runtime_version",
  // CHECK-NEXT:   buffer: 8
  // CHECK-NEXT:   } ]
  // CHECK-NEXT:   signature_defs: [ ]
  // CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")

  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<
  // CHECK-SAME:      1.000000e+00> : tensor<40xf32>}> : () -> tensor<40xf32>
  // CHECK-NEXT:    %[[fc:.*]]:2 = "tfl.fully_connected"(%[[stat]], %arg1,

// CHECK-NEXT: tfl.dequantize
}

// CHECK-LABEL: dequantize_pushdown_gather_with_reduction
func.func @dequantize_pushdown_gather_with_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<12x2xi8>} : () -> tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

// CHECK-NEXT:   %{{.*}} = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678{{[0-9]*}}:151>>, value = dense<-76> : tensor<32x3x3x3xi8>}> : () -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678{{[0-9]*}}:151>>

  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:    }, {
  // CHECK-NEXT:      shape: [ 3 ],
  // CHECK-NEXT:      type: INT8,
  // CHECK-NEXT:      buffer: 2,
  // CHECK-NEXT:      name: "tfl.pseudo_qconst",
  // CHECK-NEXT:      quantization: {
  // CHECK-NEXT:        scale: [ 0.1 ],
  // CHECK-NEXT:        zero_point: [ 0 ]
  // CHECK-NEXT:       },
  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:    }, {

  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:    }, {
  // CHECK-NEXT:      shape: [ 3 ],
  // CHECK-NEXT:      type: INT8,
  // CHECK-NEXT:      buffer: 2,
  // CHECK-NEXT:      name: "tfl.pseudo_qconst",
  // CHECK-NEXT:      quantization: {
  // CHECK-NEXT:        scale: [ 1.0 ],
  // CHECK-NEXT:        zero_point: [ 0 ]
  // CHECK-NEXT:       },
  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:    }, {

    For example, if the kernel does not support dynamic range quantization the
    graph will be converted into the following IR:

    %q_w = "tfl.pseudo_qconst"() {
         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %w = "tfl.dequantize"(%q_w) :
         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->

    %4 = "tfl.pseudo_qconst"() {qtype = tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>, value = dense<1> : tensor<1x112x112x32xi8>} : () -> tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>