>;

// This decomposition is only correct inside XLA as it ignores use_locking
// attribute.
def DecomposeResourceApplyGradientDescentOp :
  Pat<
    (TF_ResourceApplyGradientDescentOp:$src_op $resource, $alpha, $delta, BoolAttr:$_),
    (TF_AssignVariableOp
      $resource,
      (TF_SubOp
        (CreateTFReadVariableOp $src_op, $alpha, $resource),
        (TF_MulOp $alpha, $delta)
      ),
      (CreateConstBoolAttrFalse)
    )

def LegalizeMin : Pat<
  (TF_MinOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceMinOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

def LegalizeMax : Pat<
  (TF_MaxOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceMaxOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

def LegalizeProd : Pat<
  (TF_ProdOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceProdOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

    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.
    DefaultValuedOptionalAttr<BoolAttr, "false">:$is_signed
  );

  let results = (outs
    F32Tensor:$outputs
  );
}

    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.
    DefaultValuedOptionalAttr<BoolAttr, "false">:$is_signed
  );

  let results = (outs
    F32Tensor:$outputs
  );
}

    if (callable->hasAttr("tf._XlaMustCompile")) return true;
    auto noinline_attr_name = absl::StrCat("tf.", tensorflow::kNoInlineAttr);
    if (auto noinline_attr =
            callable->getAttrOfType<BoolAttr>(noinline_attr_name))
      return !noinline_attr.getValue();
    return true;
  }

  // Returns if its legal to inline 'src' region into the 'dest' region
  // attached to a TF operation.

    // Note: this information is duplicated and can be removed from the proto
    // and here once MLIR bridge phase 2 doesn't fallback to the old bridge.
    auto attr = op.getFuncOp().getArgAttrOfType<mlir::BoolAttr>(
        index, replication_attr_name);
    arg->set_is_same_data_across_replicas(attr != nullptr && attr.getValue());

    // Currently only support first dimension to be bounded dynamic.

      TF_RETURN_IF_ERROR(ConvertLocation(locations[i], node_name, debug_info));
    }
  }
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::BoolAttr& attr, AttrValue* value) {
  value->set_b(attr.getValue());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::IntegerAttr& attr, AttrValue* value) {
  value->set_i(attr.getInt());

                                     Value input, Value weight, Value input_zp,
                                     Value weight_zp, Value output,
                                     BoolAttr transpose_a,
                                     BoolAttr transpose_b) {
  // Transpose and constant-fold the weight if needed.
  if (transpose_b.getValue()) {
    Value perm = Create1DConstValue<int32_t>(builder, loc, {1, 0});

      mlir::TFL::LSTMKernelTypeAttr::get(builder_.getContext(),
                                         mlir::TFL::LSTMKernelType::FULL),
      /*asymmetric_quantize_inputs=*/mlir::BoolAttr(),
      /*input_to_input_intermediate=*/mlir::TypeAttr(),
      /*input_to_forget_intermediate=*/mlir::TypeAttr(),
      /*input_to_cell_intermediate=*/mlir::TypeAttr(),

    }
  } else if (llvm::isa<TF::BatchMatMulV2Op>(op)) {
    if (const auto adj_y = op->getAttrOfType<BoolAttr>("adj_y")) {
      is_supported_affine_op = !adj_y.getValue();
    }
  } else if (llvm::isa<TF::MatMulOp>(op)) {
    if (const auto adj_y = op->getAttrOfType<BoolAttr>("transpose_b")) {
      is_supported_affine_op = !adj_y.getValue();
    }
  }