// destination type is not inferred and must be given explicitly.
//
// Preconditions: The given value must have a ShapedType.
static Value CreateTFCastOpI32(OpBuilder *builder, Location loc, Value x,
                               BoolAttr truncate) {
  auto x_type = mlir::dyn_cast_or_null<ShapedType>(x.getType());
  if (!x_type) llvm_unreachable("unsupported type");
  Type type = x_type.clone(builder->getI32Type());

                                     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});

        legalize_to_tfl = false;
        return WalkResult::interrupt();
      }
    }
    return WalkResult::advance();
  });
  module->setAttr(kLegalizeTflVariables,
                  BoolAttr::get(context, legalize_to_tfl));
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateAnalyzeVariablesPass() {
  return std::make_unique<AnalyzeVariablesPass>();
}

}  // namespace TFL

      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();
    }
  }

  // to stateful, otherwise the op will not be executed.
  OpBuilder builder(&ctx);
  module_op.walk([&builder](Operation* op) {
    if (op->hasAttrOfType<BoolAttr>("is_stateless") &&
        ContainCalibrationStatisticsSaverOp(op)) {
      op->setAttr("is_stateless", builder.getBoolAttr(false));
    }
  });
}

std::unique_ptr<OperationPass<ModuleOp>>

      });
      return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {

    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(
        rewriter, res_type, min_value, max_value, quant_dim, num_bits,

    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(
        rewriter, input_type, min_value, max_value, quant_dim, num_bits,

    This op returns a scalar string tensor as a key for user to look for the loaded array
    and a future containing the restored tensor.
  }];

  let arguments = (ins
    TF_Tensor:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_Tensor:$array_key,
    MlrtFutureType: $tensor_future
  );
}