DefaultValuedOptionalAttr<I64Attr, "-1">:$lhs_quantization_axis,
    DefaultValuedOptionalAttr<I64Attr, "-128">:$lhs_quantization_min_val,
    DefaultValuedOptionalAttr<I64Attr, "127">:$lhs_quantization_max_val,
    DefaultValuedOptionalAttr<I64Attr, "-1">:$rhs_quantization_axis,
    DefaultValuedOptionalAttr<I64Attr, "-128">:$rhs_quantization_min_val,
    DefaultValuedOptionalAttr<I64Attr, "127">:$rhs_quantization_max_val,

  }];

  let arguments = (ins
    TFRT_ChainType:$in_ch,
    I64Attr:$num_args,
    StrAttr:$device,
    ArrayAttr:$op_attrs,
    // TODO(b/173025975): consider using DictionaryAttr after we support
    // BEF conversion for this type.
    ArrayAttr:$op_func_attrs,
    I64Attr:$op_key,
    StrAttr:$op_name
  );

  let results = (outs
    TFRT_ChainType:$out_ch
  );

    TF_Int32Tensor:$id_counts,

    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$sample_count,
    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$num_replica,
    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$max_minibatches_per_sc,
    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$max_ids_per_chip_per_sample,
    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$table_vocab_size,
    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$feature_width,

    TF_I32OrI64Tensor:$strides,
    TF_Tensor:$value,

    DefaultValuedOptionalAttr<I64Attr, "0">:$begin_mask,
    DefaultValuedOptionalAttr<I64Attr, "0">:$end_mask,
    DefaultValuedOptionalAttr<I64Attr, "0">:$ellipsis_mask,
    DefaultValuedOptionalAttr<I64Attr, "0">:$new_axis_mask,
    DefaultValuedOptionalAttr<I64Attr, "0">:$shrink_axis_mask
  );

  let results = (outs);

  }];

  let arguments = (ins
    F32Tensor:$inputs,
    F32ArrayAttr:$min,
    F32ArrayAttr:$max,
    // The quantized dimension of the inputs tensor.
    I64Attr:$axis,
    // The bitwidth of the quantization; between 2 and 16, inclusive.
    I64Attr:$num_bits,
    // Quantization range starts from 0 or 1; starts from 1 if true.
    DefaultValuedOptionalAttr<BoolAttr, "false">:$narrow_range,
    // The sign of the quantization.

    index as its identifier. It can be retrieved by _TfrtGetResource using the
    same index.
  }];

  let arguments = (ins
    TF_Tensor:$arg,
    I64Attr:$index
  );
}

def TF__TfrtGetResourceOp : TF_Op<"_TfrtGetResource",
    [Pure, TF_NoConstantFold, DeclareOpInterfaceMethods<TF_ResourceHandleAllocatorInterface>]> {

  [(TF_OneHotOp:$labels $sparse_labels,
     (TF_ConstOp (GetDimSizeAsI32<1> $features, $src_op__0)),
     (TF_ConstOp (GetScalarOfType<1> $features)),
     (TF_ConstOp (GetScalarOfType<0> $features)),
     ConstantAttr<I64Attr, "1">),
   (TF_SelectV2Op:$zero_or_nan
     (TF_LogicalAndOp
       (TF_LessEqualOp
         (TF_ConstOp (GetScalarOfType<0> $sparse_labels)), $sparse_labels),
       (TF_LessOp $sparse_labels,

    Set a tensor in resource array.

    arg: the tensor to be set in the resource array.
    index: the index in the resource array
  }];

  let arguments = (ins
    TFTensorType:$arg,
    I64Attr:$index
  );

  let results = (outs);

  let assemblyFormat = "operands attr-dict";
}

def GetResourceOp : TensorflowMlrt_Op<"get_resource", []> {
  let summary = "get a tensor in resource array";

def ValueToVariadic: NativeCodeCall<"SmallVector<Value, 1>{$0}">;
def : Pat<(TF_AllToAllOp AnyRankedTensor:$input, (ConstantLikeMatcher ElementsAttr:$group_assignment), I64Attr:$concat_dimension, $split_dimension, $split_count),
          (MHLO_AllToAllOp (ValueToVariadic $input), $split_dimension, $concat_dimension, $split_count, (CastElementsToI64Elements $group_assignment), (NullChannelHandleAttr))>;

lower bound 0 is included in the range, while the upper bound 1 is excluded.
  }];

  let arguments = (ins
    TFL_I32Tensor:$shape,
    DefaultValuedOptionalAttr<I64Attr, "0">:$seed,
    DefaultValuedOptionalAttr<I64Attr, "0">:$seed2
  );

   let results = (outs
    TFL_TensorOf<[F32]>:$out);
  let hasOptions = 1;
  let customOption = "RandomOptions";
}