return failure();

    for (auto &use : llvm::make_early_inc_range(op.getControl().getUses()))
      use.getOwner()->eraseOperand(use.getOperandNumber());

    rewriter.eraseOp(op);

    return success();
  }
};

// This pattern matches and removes IslandOps with no inner ops, no control
// operands, one data result and no control result user. The single data result

  return failure();
}

// TF Lite doesn't support Assert, we just drop the assert from the graph.
LogicalResult ConvertTFAssertOp::matchAndRewrite(
    Operation* op, PatternRewriter& rewriter) const {
  rewriter.eraseOp(op);
  return success();
}

// Legalize unidirectional sequence lstm.
struct LegalizeUnidirectionalSequenceLstm : public RewritePattern {
  explicit LegalizeUnidirectionalSequenceLstm(MLIRContext* context)

    for (Operation *user : llvm::make_early_inc_range(old_result.getUsers()))
      rewriter.replaceOp(user, ValueRange(new_result));
  }

  // Erase original unpack operation.
  rewriter.eraseOp(op.getOperation());

  return success();
}

}  // namespace

void UnpackOp::getCanonicalizationPatterns(RewritePatternSet &results,

                                PatternRewriter& rewriter) const override {
    ElementsAttr cst;
    if (matchPattern(op.getCondition(), m_Constant(&cst))) {
      if (cst.getValues<bool>()[0]) {
        rewriter.eraseOp(op);
        return success();
      }
    }
    return failure();
  }
};
}  // namespace

void AssertOp::getCanonicalizationPatterns(RewritePatternSet& results,

          conv_op.getLoc(), new_conv.getResult(),
          rewriter.getI64TensorAttr(transpose_order));
      conv_op->replaceAllUsesWith(output_transpose);
      rewriter.eraseOp(conv_op);
    } else {
      SmallVector<int32_t, 4> output_shape_i32;
      for (int64_t dim :
           mlir::cast<RankedTensorType>(conv_op.getResult().getType())
               .getShape()) {

      values_slice_op.erase();
    }
    if (!indices.use_empty()) {
      auto indices_slice_op = llvm::dyn_cast_or_null<TFL::SliceOp>(
          indices.getUses().begin().getUser());
      indices_slice_op.getResult().replaceAllUsesWith(top_k_op.getIndices());
      indices_slice_op.erase();
    }
    op.erase();
    return success();
  }
};

  func_op.eraseArguments(args_to_erase_bit_vector);

  // Erases entries in "tf._input_shapes" attribute of `func_op` that correspond
  // to the erased arguments.
  if (auto input_shapes_attr =
          func_op->getAttrOfType<ArrayAttr>(kTfInputShapesAttr);
      input_shapes_attr) {
    // Construct a new array of input shapes excluding the input shapes of the
    // erased arguments.
    SmallVector<Attribute> updated_input_shapes_attr;

                "of indices");
    }
  }

  if ((indices_rank == 0) || (indices_rank == batch_dims_i)) {
    // Scalar indices (output is rank(params) - 1).
    // Erase shape[axis]
    shape.erase(shape.begin() + axis_i);
  } else if (indices_rank == 1) {
    // Vector indices (output is rank(params)).
    // Copy indices.shape into params.shape[axis]

    for (auto it :
         llvm::zip(control_node_op.getOutputs(), cloned_inner->getResults())) {
      std::get<0>(it).replaceAllUsesWith(std::get<1>(it));
    }
    rewriter.eraseOp(outer_op);
  }
  return control_edges;
}

}  // namespace

namespace tflite {

bool MlirToFlatBufferTranslateFunction(mlir::ModuleOp module,

    }
  }
  return result;
}

void EraseReturnOperands(Region* region, llvm::BitVector& erase) {
  for (Block& block : region->getBlocks()) {
    for (Operation& op : block.getOperations()) {
      if (!op.hasTrait<OpTrait::ReturnLike>()) continue;
      op.eraseOperands(erase);
    }
  }
}

// Erases the given results from an operation, similar to what
// Operation::eraseArguments does (but for results).