// CHECK-SAME: tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>
func.func @func_attr() attributes {tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>} {
  func.return
}

// CHECK-LABEL: func @nested_func_attr
// CHECK-SAME: tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random", nested = #tf_type.func<@symbol_b, {attr2 = true, attr3 = 8.000000e+00 : f32}>}>

//
// Use Group to collect several key-value pairs under a single
// key on a log line, or as the result of LogValue
// in order to log a single value as multiple Attrs.
func Group(key string, args ...any) Attr {
	return Attr{key, GroupValue(argsToAttrSlice(args)...)}
}

func argsToAttrSlice(args []any) []Attr {
	var (
		attr  Attr
		attrs []Attr
	)
	for len(args) > 0 {

        extraAttributes.contains(attr2)
        !extraAttributes.contains(attr1)
    }

    def "collects extra attributes, two candidates"() {
        def attr1 = Attribute.of("foo", String)
        def attr2 = Attribute.of("bar", String)
        def attr3 = Attribute.of("baz", String)

        given:
        ImmutableAttributes[] candidates = [
            candidate(ImmutableMap.of(attr1, "v1", attr2, "v2")),

        hasExtraAttribute(attr2)
        doesNotHaveExtraAttribute(attr1)
    }

    def "collects extra attributes, two candidates"() {
        def attr1 = Attribute.of("foo", String)
        def attr2 = Attribute.of("bar", String)
        def attr3 = Attribute.of("baz", String)

        given:
        candidate {
            attribute(attr1, "v1")
            attribute(attr2, "v2")
        }
        candidate {

  %result = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<*xf32> {attr3 = 32 : i64, tf_executor.attr_fetch = "some_value"}
  } {attr2 = 32 : i64, tf_executor.attr1 = "value1"}

// CHECK:      tf_executor.graph {
// CHECK-NEXT:    tf_executor.fetch %{{.*}} : tensor<*xf32> {attr3 = 32 : i64, tf_executor.attr_fetch = "some_value"}
// CHECK-NEXT: } {attr2 = 32 : i64, tf_executor.attr1 = "value1"}
  func.return %result : tensor<*xf32>
}

node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "args_1"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 1
    }
  }
}

}

// CHECK:        op: "NoOp"
// CHECK-NEXT:   attr
// CHECK-NEXT:     key: "_f"
// CHECK-NEXT:     value
// CHECK-NEXT:       func
// CHECK-NEXT:         name: "original_callee"
// CHECK-NEXT:         attr
// CHECK-NEXT:           key: "attr2"
// CHECK-NEXT:           value
// CHECK-NEXT:             b: true
// CHECK:              attr
// CHECK-NEXT:           key: "attr3"
// CHECK-NEXT:           value

    device: The tensorflow device. eg. "/CPU:0"
    op_attrs: The tensorflow attributes excluding the func attrs.
    op_func_attrs: The func attrs.
    op_name: The tensorflow op name. eg. "tf.AddV2"

    Example:
      %out_ch = tfrt_fallback_async.createop(%in_ch) key(0) device("/CPU:0")
        "some.op"() {attr1 = value, attr2 = value2} {f_attr1 = "fn1", f_attr2 = "fn2"} num_args(1)
  }];

  let arguments = (ins

node {
  name: "save/SaveV2/shape_and_slices"
  op: "Const"
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
        }
      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING

    device: The tensorflow device. eg. "/CPU:0"
    op_attrs: The tensorflow attributes excluding the func attrs.
    op_name: The tensorflow op name. eg. "tf.AddV2"

    Example:
      tfrt_fallback_sync.createop() key(0) device("/CPU:0")
        "some.op"() {attr1 = value, attr2 = value2} {f_attr1 = "fn1", f_attr2 = "fn2"} num_args(1)
  }];

  let arguments = (ins
    StrAttr:$node_def,