cst.erase();
      }

      if (result.needs_densify) {
        auto value = op->getOperand(operand);
        auto densify =
            builder.create<DensifyOp>(op->getLoc(), value.getType(), value);
        value.replaceAllUsesWith(densify);
        densify.setOperand(value);
      }
    }
  });
}

}  // namespace

  let summary = "Convert dense tensor to sparse format.";
  let description = [{
      This pass encodes sparse weights in the model in the proper format, and adds
      Densify() op if necessary. The general algorithm is:
        1. Get list of operands (weights) of an op that can be sparse.
        2. Get list of supported block configurations of the op.
        3. Calculate random sparsity of the weight.

  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,
  NON_MAX_SUPPRESSION_V5 = 121,
  SCATTER_ND = 122,
  SELECT_V2 = 123,
  DENSIFY = 124,
  SEGMENT_SUM = 125,
  BATCH_MATMUL = 126,
  PLACEHOLDER_FOR_GREATER_OP_CODES = 127,
  CUMSUM = 128,
  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,

  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,
  NON_MAX_SUPPRESSION_V5 = 121,
  SCATTER_ND = 122,
  SELECT_V2 = 123,
  DENSIFY = 124,
  SEGMENT_SUM = 125,
  BATCH_MATMUL = 126,
  PLACEHOLDER_FOR_GREATER_OP_CODES = 127,
  CUMSUM = 128,
  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,

  );

  let results = (outs TFL_TensorOf<[QI4, QI8, QUI8, QI16, TFL_Quint8]>:$output);
}

def TFL_DensifyOp: TFL_Op<"densify", [
    Pure,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>]> {
  let summary = "Densify operator";

  let description = [{
    Converts sparse tensor to dense format.
  }];

  func.return %0 : tensor<1x64x84x31xf32>
}

// -----

// CHECK-LABEL: testDensify
func.func @testDensify(%arg0: tensor<? x f32>) -> tensor<? x f32> {
  // CHECK: "tfl.densify"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>
  %0 = "tfl.densify"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// -----

// during memory allocation) further ensures that chunks it identifies as "dense" are
// immediately eligible for being backed by huge pages. Note that for the most part these
// density heuristics are best-effort heuristics. It's totally possible (but unlikely)
// that a chunk that just became dense is scavenged in the case of a race between memory
// allocation and scavenging.
//

// An Op is an ARM64 opcode.
type Op uint16

// NOTE: The actual Op values are defined in tables.go.
// They are chosen to simplify instruction decoding and
// are not a dense packing from 0 to N, although the
// density is high, probably at least 90%.

func (op Op) String() string {
	if op >= Op(len(opstr)) || opstr[op] == "" {
		return fmt.Sprintf("Op(%d)", int(op))
	}
	return opstr[op]
}

// An Inst is a single instruction.

      "dev": true,
      "engines": {
        "node": ">=8"
      }
    },
    "node_modules/reusify": {
      "version": "1.0.4",
      "resolved": "https://registry.npmjs.org/reusify/-/reusify-1.0.4.tgz",
      "integrity": "sha512-U9nH88a3fc/ekCF1l0/UP1IosiuIjyTh7hBvXVMHYgVcfGvt897Xguj2UOLDeI5BG2m7/uwyaLVT6fbtCwTyzw==",
      "dev": true,
      "engines": {

    "MATRIX_SET_DIAG",
    "ROUND",
    "HARD_SWISH",
    "IF",
    "WHILE",
    "NON_MAX_SUPPRESSION_V4",
    "NON_MAX_SUPPRESSION_V5",
    "SCATTER_ND",
    "SELECT_V2",
    "DENSIFY",
    "SEGMENT_SUM",
    "BATCH_MATMUL",
    "PLACEHOLDER_FOR_GREATER_OP_CODES",
    "CUMSUM",
    "CALL_ONCE",
    "BROADCAST_TO",
    "RFFT2D",
    "CONV_3D",
    "IMAG",
    "REAL",