*status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;
    }
    shape.push_back(dim);
  }
  return shape;
}

}  // namespace

extern "C" {

TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(

  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;
  // Returns the device where the tensor was placed.

  }
  auto imm_ctx = dyn_cast<ImmediateExecutionContext>(ctx);
  AbstractTensorPtr minus_1(imm_ctx->CreateInt32Scalar(-1));
  ImmediateTensorHandlePtr dim(imm_ctx->CreateLocalHandle(minus_1.get()));
  AbstractTensorHandle* expand_dims_outputs;
  TF_RETURN_IF_ERROR(
      ops::ExpandDims(ctx, vec, dim.get(), &expand_dims_outputs, "ExpandDims"));
  TF_RETURN_IF_ERROR(
      ops::Mul(ctx, expand_dims_outputs, mat, &outputs[0], "Mul"));

  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_.attrs.Set(attr_name, proto);
  return parent_op_->SetAttrShape(attr_name, dims, num_dims);
}
Status TapeOperation::SetAttrFunction(const char* attr_name,

    return nullptr;
  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);
    Status status = tensorflow::PartialTensorShape::MakePartialShape(
        reinterpret_cast<int64_t*>(shape.dim_sizes), shape.num_dims,
        &partial_shape);
    if (!status.ok()) {
      tsl::Set_TF_Status_from_Status(s, status);
      return nullptr;
    }

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info);

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow

      TF_ASSERT_OK(s);

      EXPECT_EQ(DT_FLOAT, in_props.dtype());
      EXPECT_FALSE(in_props.shape().unknown_rank());
      EXPECT_EQ(2, in_props.shape().dim_size());
      EXPECT_EQ(10, in_props.shape().dim(0).size());
      EXPECT_EQ(1, in_props.shape().dim(1).size());

      for (int i = 0; i < in_props_buf.size(); i++)
        TF_DeleteBuffer(in_props_buf[i]);
    }
  }

#undef EXPECT_TF_META

TEST(CAPI, TestTensorAligned) {
  int64_t dim = 7;
  size_t tensor_size_bytes = dim * TF_DataTypeSize(TF_FLOAT);
  TF_Tensor* a = TF_AllocateTensor(
      /*dtype=*/TF_FLOAT, /*dims=*/&dim, /*num_dims=*/1,
      /*len=*/tensor_size_bytes);
  float* data = reinterpret_cast<float*>(TF_TensorData(a));
  for (int i = 0; i < dim; ++i) {
    data[i] = 0;
  }
  if (TF_TensorDefaultAlignment() > 0) {

    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

}  // namespace

  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);
    ASSERT_EQ(dims[j], dim_analytical);
    num_elem_analytical *= dim_analytical;
  }

  float* danalytical = new float[num_elem_analytical]{0};
  memcpy(&danalytical[0], TF_TensorData(analytical_tensor),