// CHECK-SAME: %arg21: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg22: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg23: tensor<!tf_type.resource<tensor<512x640xf32>>>
  // CHECK-SAME: %arg24: tensor<!tf_type.resource<tensor<640x640xf32>>>
  // CHECK-SAME: %arg25: tensor<!tf_type.resource<tensor<4096xf32>>>
  // CHECK-SAME: %arg26: tensor<!tf_type.resource<tensor<640x4096xf32>>>

    // CHECK-SAME:       device_ordinal = 0
    // CHECK-SAME:       device_type = "TPU"
    // CHECK-SAME:       key = "host_compute_channel_0_retvals"
    // CHECK:          "tf_device.cluster"
    // CHECK:            %[[A_OUTPUT:[0-9]*]] = "tf.A"
    // CHECK:            %[[HOST_OUTPUT:[0-9]*]] = "tf._XlaHostComputeMlir"()
    // CHECK-SAME:       recv_key = "host_compute_channel_0_retvals"

    // CHECK:      metadata
    // CHECK-SAME: args
    // CHECK-SAME: dtype: DT_INT32
    // CHECK-SAME: shape
    // CHECK-SAME: dim
    // CHECK-SAME: size: 8
    // CHECK-SAME: kind: PARAMETER
    // CHECK-SAME: sharding
    // CHECK-SAME: type: MAXIMAL
    // CHECK-SAME: tile_assignment_dimensions: 1
    // CHECK-SAME: tile_assignment_devices: 0
    // CHECK-SAME: retvals
    // CHECK-SAME: sharding

class OperandsSameElementTypeConstraintBase<string op> :
  PredOpTrait<op # " operands have same element type",
    OperandsSameElementTypeConstraintBasePred>;

// This is a constraint for most of the binary ops, e.g., add, mul, div, etc.
// Binary ops lhs & rhs should have the same value type, and is capable to
// compare quantization types as well.
def BinaryOpSameElementTypeConstraint :

func.func @testHashTableAndInitializeTableToV2(%arg0: tensor<!tf_type.string>) {
  // CHECK: [[handle:%.*]] = "tf.HashTableV2"()
  // CHECK-SAME: container = ""
  // CHECK-SAME: key_dtype = !tf_type.string
  // CHECK-SAME: shared_name = "table"
  // CHECK-SAME: value_dtype = i32
  // CHECK-SAME: device = ""
  // CHECK-SAME: () -> tensor<!tf_type.resource>

}

// CHECK-LABEL: reduce_window_with_max
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x9x10x3x!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK-SAME: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK: %[[MAX_POOL:.*]] = "tfl.max_pool_2d"(%[[ARG0]])

like continual Series between two parallel right Lines _af_ and _gm_ with the same distances between their Centers, and illuminated by the same sorts of Rays, that is the Circle _ag_ with the same sort by which the corresponding Circle AG was illuminated, and the Circle _bh_ with the same sort by which the corresponding Circle BH was illuminated, and the rest of the Circles _ci_, _dk_, _el_, _fm_ respectively, with the same sorts of Rays by which the several corresponding Circles CJ, DK, EL, FM were...

  // CHECK: "tfl.unidirectional_sequence_lstm"(%arg0,
  // CHECK-SAME: %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15,
  // CHECK-SAME: %arg16, %arg17, %arg18, %arg19, %arg20, %arg21, %arg22, %arg23) <{cell_clip = 1.000000e+00 : f32, fused_activation_function = "NONE", time_major = false}> :
  // CHECK-SAME: (tensor<?x?x?xf32>,

<pre>
[...]Point{{1.5, -3.5}, {0, 0}}     // same as [...]Point{Point{1.5, -3.5}, Point{0, 0}}
[][]int{{1, 2, 3}, {4, 5}}          // same as [][]int{[]int{1, 2, 3}, []int{4, 5}}
[][]Point{{{0, 1}, {1, 2}}}         // same as [][]Point{[]Point{Point{0, 1}, Point{1, 2}}}
map[string]Point{"orig": {0, 0}}    // same as map[string]Point{"orig": Point{0, 0}}
map[Point]string{{0, 0}: "orig"}    // same as map[Point]string{Point{0, 0}: "orig"}

{"Name":"BIF","Bits":"0|Q|1|0|1|1|1|0|1|1|1|Rm:5|0|0|0|1|1|1|Rn:5|Rd:5","Arch":"Three registers of the same type variant","Syntax":"BIF <Vd>.<T>, <Vn>.<T>, <Vm>.<T>","Code":"","Alias":""},