void ReshapeSparseTensor(OpKernelContext *context,
                         const Tensor &input_indices_in,
                         const Tensor &input_shape_in,
                         const Tensor &target_shape_in, int output_indices_idx,
                         int output_shape_idx) {
  OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices_in.shape()),
              errors::InvalidArgument(
                  "Input indices should be a matrix but received shape ",
                  input_indices_in.shape().DebugString()));
  OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape_in.shape()),
              errors::InvalidArgument(
                  "Input shape should be a vector but received shape ",
                  input_shape_in.shape().DebugString()));
  OP_REQUIRES(context, TensorShapeUtils::IsVector(target_shape_in.shape()),
              errors::InvalidArgument(
                  "Target shape should be a vector but received shape ",
                  target_shape_in.shape().DebugString()));

  const int64_t output_rank = target_shape_in.NumElements();
  const TensorShape input_shape(input_shape_in.vec<int64>());
  const int64_t dense_size = input_shape.num_elements();
  const int64_t nnz = input_indices_in.shape().dim_size(0);

  // Compute the output shape. Determine product of specified dimensions, and
  // find the index of the unspecified one.
  TensorShape output_shape;
  int64_t product = 1;
  int unknown_index = -1;
  auto target_shape = target_shape_in.vec<int64>();
  for (int d = 0; d < output_rank; ++d) {
    const int64_t size = target_shape(d);
    if (size == -1) {
      OP_REQUIRES(
          context, unknown_index == -1,
          errors::InvalidArgument("only one output dimension may be -1, "
                                  "not both ",
                                  unknown_index, " and ", d));
      unknown_index = d;
      output_shape.AddDim(1);
    } else {
      OP_REQUIRES(context, size >= 0,
                  errors::InvalidArgument("size ", d,
                                          " must be non-negative, not ", size));
      product *= size;
      output_shape.AddDim(size);
    }
  }
  if (unknown_index != -1) {
    OP_REQUIRES(
        context, product > 0,
        errors::InvalidArgument("reshape cannot infer the missing "
                                "input size for an empty tensor unless all "
                                "specified input sizes are non-zero"));
    const int64_t missing = dense_size / product;
    OP_REQUIRES(
        context, product * missing == dense_size,
        errors::InvalidArgument(
            "Input to reshape is a SparseTensor with ", dense_size,
            " dense values, but the requested shape requires a multiple of ",
            product, ". input_shape=", input_shape.DebugString(),
            " output_shape=", output_shape.DebugString()));
    output_shape.set_dim(unknown_index, missing);
  }

  OP_REQUIRES(
      context, output_shape.num_elements() == dense_size,
      errors::InvalidArgument("Input to reshape is a tensor with ", dense_size,
                              " dense values, but the requested shape has ",
                              output_shape.num_elements(),
                              ". input_shape=", input_shape.DebugString(),
                              " output_shape=", output_shape.DebugString()));

  // Optimize for reshaping to the same shape.
  if (input_shape == output_shape) {
    context->set_output(output_indices_idx, input_indices_in);
    context->set_output(output_shape_idx, input_shape_in);
    return;
  }

  Tensor *result_shape = nullptr;
  OP_REQUIRES_OK(context, context->allocate_output(output_shape_idx,
                                                   TensorShape({output_rank}),
                                                   &result_shape));
  auto output_shape_vec = result_shape->vec<int64>();
  for (int j = 0; j < output_shape.dims(); ++j) {
    output_shape_vec(j) = output_shape.dim_size(j);
  }

  Tensor *result_indices = nullptr;
  OP_REQUIRES_OK(context,
                 context->allocate_output(output_indices_idx,
                                          TensorShape({nnz, output_rank}),
                                          &result_indices));
  if (nnz > 0) {
    OP_REQUIRES(
        context, dense_size > 0 && product > 0,
        errors::InvalidArgument(
            "Input tensor has ", nnz, " non zero elements but input shape (",
            input_shape.DebugString(), ") or output shape (",
            output_shape.DebugString(), ") is empty"));
    OP_REQUIRES_OK(context, functor::ReshapeSparseTensorFunctor<Device>()(
                                context, input_shape, output_shape,
                                input_indices_in.matrix<int64>(),
                                result_indices->matrix<int64>()));
  }
}
void ReshapeSparseTensor(OpKernelContext *context,
                         const Tensor &input_indices_in,
                         const Tensor &input_shape_in,
                         const Tensor &target_shape_in, int output_indices_idx,
                         int output_shape_idx) {
  OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices_in.shape()),
              errors::InvalidArgument(
                  "Input indices should be a matrix but received shape ",
                  input_indices_in.shape().DebugString()));
  OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape_in.shape()),
              errors::InvalidArgument(
                  "Input shape should be a vector but received shape ",
                  input_shape_in.shape().DebugString()));
  OP_REQUIRES(context, TensorShapeUtils::IsVector(target_shape_in.shape()),
              errors::InvalidArgument(
                  "Target shape should be a vector but received shape ",
                  target_shape_in.shape().DebugString()));

  const int64_t output_rank = target_shape_in.NumElements();
  const TensorShape input_shape(input_shape_in.vec<int64>());
  const int64_t dense_size = input_shape.num_elements();
  const int64_t nnz = input_indices_in.shape().dim_size(0);

   
   
  TensorShape output_shape;
  int64_t product = 1;
  int unknown_index = -1;
  auto target_shape = target_shape_in.vec<int64>();
  for (int d = 0; d < output_rank; ++d) {
    const int64_t size = target_shape(d);
    if (size == -1) {
      OP_REQUIRES(
          context, unknown_index == -1,
          errors::InvalidArgument("only one output dimension may be -1, "
                                  "not both ",
                                  unknown_index, " and ", d));
      unknown_index = d;
      output_shape.AddDim(1);
    } else {
      OP_REQUIRES(context, size >= 0,
                  errors::InvalidArgument("size ", d,
                                          " must be non-negative, not ", size));
      product *= size;
      output_shape.AddDim(size);
    }
  }
  if (unknown_index != -1) {
    OP_REQUIRES(
        context, product > 0,
        errors::InvalidArgument("reshape cannot infer the missing "
                                "input size for an empty tensor unless all "
                                "specified input sizes are non-zero"));
    const int64_t missing = dense_size / product;
    OP_REQUIRES(
        context, product * missing == dense_size,
        errors::InvalidArgument(
            "Input to reshape is a SparseTensor with ", dense_size,
            " dense values, but the requested shape requires a multiple of ",
            product, ". input_shape=", input_shape.DebugString(),
            " output_shape=", output_shape.DebugString()));
    output_shape.set_dim(unknown_index, missing);
  }

  OP_REQUIRES(
      context, output_shape.num_elements() == dense_size,
      errors::InvalidArgument("Input to reshape is a tensor with ", dense_size,
                              " dense values, but the requested shape has ",
                              output_shape.num_elements(),
                              ". input_shape=", input_shape.DebugString(),
                              " output_shape=", output_shape.DebugString()));

   
  if (input_shape == output_shape) {
    context->set_output(output_indices_idx, input_indices_in);
    context->set_output(output_shape_idx, input_shape_in);
    return;
  }

  Tensor *result_shape = nullptr;
  OP_REQUIRES_OK(context, context->allocate_output(output_shape_idx,
                                                   TensorShape({output_rank}),
                                                   &result_shape));
  auto output_shape_vec = result_shape->vec<int64>();
  for (int j = 0; j < output_shape.dims(); ++j) {
    output_shape_vec(j) = output_shape.dim_size(j);
  }

  Tensor *result_indices = nullptr;
  OP_REQUIRES_OK(context,
                 context->allocate_output(output_indices_idx,
                                          TensorShape({nnz, output_rank}),
                                          &result_indices));
  if (nnz > 0) {
    OP_REQUIRES(
        context, dense_size > 0 && product > 0,
        errors::InvalidArgument(
            "Input tensor has ", nnz, " non zero elements but input shape (",
            input_shape.DebugString(), ") or output shape (",
            output_shape.DebugString(), ") is empty"));
    OP_REQUIRES_OK(context, functor::ReshapeSparseTensorFunctor<Device>()(
                                context, input_shape, output_shape,
                                input_indices_in.matrix<int64>(),
                                result_indices->matrix<int64>()));
  }
}