Method Compute

tensorflow/core/kernels/tensor_array_ops.cc:1234–1350

      : OpKernel(context) {}

  void Compute(OpKernelContext* ctx) override {
    OP_REQUIRES_OK(ctx, SetupFlowControlInputs(ctx, true));

    TensorArray* tensor_array = nullptr;
    OP_REQUIRES_OK(ctx, GetTensorArray(ctx, &tensor_array));
    core::ScopedUnref unref(tensor_array);
    const Tensor* tensor_value;
    OP_REQUIRES_OK(ctx, ctx->input("value", &tensor_value));
    const Tensor* tensor_lengths;
    OP_REQUIRES_OK(ctx, ctx->input("lengths", &tensor_lengths));

    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(tensor_lengths->shape()),
                errors::InvalidArgument(
                    "Expected lengths to be a vector, received shape: ",
                    tensor_lengths->shape().DebugString()));
    OP_REQUIRES(ctx,
                FastBoundsCheck(tensor_lengths->NumElements(),
                                std::numeric_limits<int32>::max()),
                errors::InvalidArgument(
                    "Expected lengths to have < max int32 entries"));

    int32 num_tensors = static_cast<int32>(tensor_lengths->NumElements());
    auto tensor_lengths_t = tensor_lengths->vec<int64>();
    std::vector<int64> cumulative_lengths;
    cumulative_lengths.reserve(num_tensors);
    int64 total_length = 0;
    for (int i = 0; i < num_tensors; ++i) {
      total_length += tensor_lengths_t(i);
      cumulative_lengths.push_back(total_length);
    }

    OP_REQUIRES(
        ctx, TensorShapeUtils::IsVectorOrHigher(tensor_value->shape()),
        errors::InvalidArgument(
            "Expected value to be at least a vector, but received shape: ",
            tensor_value->shape().DebugString()));

    OP_REQUIRES(
        ctx, total_length == tensor_value->shape().dim_size(0),
        errors::InvalidArgument("Expected sum of lengths to be equal to "
                                "values.shape[0], but sum of lengths is ",
                                total_length, " and value's shape is: ",
                                tensor_value->shape().DebugString()));
    int64 elements_per_row =
        (total_length == 0) ? 0 : (tensor_value->NumElements() / total_length);

    int32 array_size;
    OP_REQUIRES_OK(ctx, tensor_array->Size(&array_size));
    bool dynamic_size = tensor_array->HasDynamicSize();

    std::vector<TensorShape> element_shapes(num_tensors, tensor_value->shape());
    for (int32 i = 0; i < num_tensors; ++i) {
      element_shapes[i].set_dim(0, tensor_lengths_t(i));
    }

    // If dynamic size, we may have to resize the TensorArray to fit.
    if (dynamic_size && array_size < num_tensors) {
      array_size = num_tensors;