// expected-error@+1 {{Denied unregistered attribute was found: denied_attr}}
  %0:2 = "tf.FusedN"(%arg0, %arg1, %arg2) {A=0:index, denied_attr} : (tensor<1x2x3x4x!tf_type.string>, tensor<f32>, tensor<f32>) -> (tensor<1x2x3x4x!tf_type.string>, tensor<f32>)
  func.return %0#1 : tensor<f32>

// CHECK-NEXT:   "tf.FusedN"(%arg0, %arg1, %arg2) {A = 0 : index, denied_attr}
}

// CHECK-LABEL: quantized_tensor

    this kernel in the inference graph.

    *   *Solution*: The user should define a new TF op, which corresponds to the
        fused kernel, with composition, and use this op to build the model for
        both training and inference. For the platforms where a fused kernel is
        not available, the execution will use the composition instead.

## Gradient
(TODO)

## Authoring Op Composition in Python

          contraction,
          "fused operation must be nested inside a function, If or While");
    }

    // If the contraction is used in multiple places, fusing it will only create
    // more contraction nodes, which is slower.
    if (!contraction.getResult().hasOneUse())
      return rewriter.notifyMatchFailure(contraction,
                                         "result is used by multiple ops");

constexpr char kCoupleInputForgetGates[] = "CoupleInputForgetGates";

// A utility class that enables the conversion of the LSTMCellSimple composite
// op into a fused TFL LSTM op. The fused op is contained within a FuncOp
// that also contains other supporting ops needed to construct the operands for
// the fused op. The caller provides the containing FuncOp as input with
// arguments specifying the input, weight, projection and bias.

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

### 5. (optional) Any other info / logs

  // DT_FLOAT, DT_HALF, DT_QINT8, and DT_QUINT8. When DT_HALF is used, the
  // `weight_quantization` flag needs to set to true. When DT_QUINT8 is used,
  // the `weight_quantization` flag needs to set to false.
  tensorflow::DataType inference_type = tensorflow::DT_FLOAT;

  // The input and output data type during inference. This flag is only used
  // when `inference_type` is different from DT_FLOAT. This flag can only be set

	HasAVX512IFMA       bool // Advanced vector extension 512 Integer Fused Multiply Add
	HasAVX512VBMI       bool // Advanced vector extension 512 Vector Byte Manipulation Instructions
	HasAVX5124VNNIW     bool // Advanced vector extension 512 Vector Neural Network Instructions Word variable precision
	HasAVX5124FMAPS     bool // Advanced vector extension 512 Fused Multiply Accumulation Packed Single precision

    // TFLite fused embedding_lookup op.
    ConvertEmbeddedLookupFunc convert_embedded_lookup(func);
    if (failed(convert_embedded_lookup.VerifySignature())) return;
    func.eraseBody();
    func.addEntryBlock();
    convert_embedded_lookup.RewriteFunc();
  } else if (attr.getValue() == mlir::TFL::kLstmCellSimple) {
    // Check if the lstm cell simple can be fused, if not, we just don't do
    // anything.

                 "comma separated list of allowlisted functions to be quantized. Only used in tests">,
      Option<"quantize_signed_", "quantize-signed", "bool", "false",
             "signed inference type. Only used in tests">,
      Option<"activation_number_of_bits_", "activation-number-of-bits", "int", "8",
             "number of bits for inference type. Only used in tests">,

<{host_mlir_module = "#loc1 = loc(\22Reshape:\22)\0A#loc2 = loc(\22Reshape_4\22)\0A#loc3 = loc(\22Reshape\22)\0A#loc9 = loc(fused[#loc1, #loc2, #loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \...