Tensor.cpp

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// ----------------------------------------------------------------------------
// -                        CloudViewer: www.cloudViewer.org                  -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.cloudViewer.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
 
#include "cloudViewer/core/Tensor.h"
 
#include <Logging.h>
#include <Optional.h>
 
#include <numeric>
#include <sstream>
 
#include "cloudViewer/core/AdvancedIndexing.h"
#include "cloudViewer/core/Blob.h"
#include "cloudViewer/core/CUDAUtils.h"
#include "cloudViewer/core/DLPack.h"
#include "cloudViewer/core/Device.h"
#include "cloudViewer/core/Dispatch.h"
#include "cloudViewer/core/Dtype.h"
#include "cloudViewer/core/ShapeUtil.h"
#include "cloudViewer/core/SizeVector.h"
#include "cloudViewer/core/TensorCheck.h"
#include "cloudViewer/core/TensorFunction.h"
#include "cloudViewer/core/TensorKey.h"
#include "cloudViewer/core/kernel/Arange.h"
#include "cloudViewer/core/kernel/IndexReduction.h"
#include "cloudViewer/core/kernel/Kernel.h"
#include "cloudViewer/core/linalg/Det.h"
#include "cloudViewer/core/linalg/Inverse.h"
#include "cloudViewer/core/linalg/LU.h"
#include "cloudViewer/core/linalg/LeastSquares.h"
#include "cloudViewer/core/linalg/Matmul.h"
#include "cloudViewer/core/linalg/SVD.h"
#include "cloudViewer/core/linalg/Solve.h"
#include "cloudViewer/core/linalg/Tri.h"
#include "cloudViewer/t/io/NumpyIO.h"
 
namespace cloudViewer {
namespace core {
 
static DLDataTypeCode DtypeToDLDataTypeCode(const Dtype& dtype) {
    if (dtype == core::Float32) return DLDataTypeCode::kDLFloat;
    if (dtype == core::Float64) return DLDataTypeCode::kDLFloat;
    if (dtype == core::Int8) return DLDataTypeCode::kDLInt;
    if (dtype == core::Int16) return DLDataTypeCode::kDLInt;
    if (dtype == core::Int32) return DLDataTypeCode::kDLInt;
    if (dtype == core::Int64) return DLDataTypeCode::kDLInt;
    if (dtype == core::UInt8) return DLDataTypeCode::kDLUInt;
    if (dtype == core::UInt16) return DLDataTypeCode::kDLUInt;
    if (dtype == core::UInt32) return DLDataTypeCode::kDLUInt;
    if (dtype == core::UInt64) return DLDataTypeCode::kDLUInt;
    if (dtype == core::Bool) return DLDataTypeCode::kDLBool;
    utility::LogError("Unsupported data type");
    return DLDataTypeCode();
}
 
static Dtype DLDataTypeToDtype(const DLDataType& dltype) {
    if (dltype.lanes != 1) {
        utility::LogError("Only supports lanes == 1, but lanes == {}",
                          dltype.lanes);
    }
    switch (dltype.code) {
        case DLDataTypeCode::kDLBool:
            return core::Bool;
            break;
        case DLDataTypeCode::kDLUInt:
            switch (dltype.bits) {
                case 8:
                    return core::UInt8;
                case 16:
                    return core::UInt16;
                case 32:
                    return core::UInt32;
                case 64:
                    return core::UInt64;
                default:
                    utility::LogError("Unsupported kDLUInt bits {}",
                                      dltype.bits);
            }
            break;
        case DLDataTypeCode::kDLInt:
            switch (dltype.bits) {
                case 8:
                    return core::Int8;
                case 16:
                    return core::Int16;
                case 32:
                    return core::Int32;
                case 64:
                    return core::Int64;
                default:
                    utility::LogError("Unsupported kDLInt bits {}",
                                      dltype.bits);
            }
            break;
        case DLDataTypeCode::kDLFloat:
            switch (dltype.bits) {
                case 32:
                    return core::Float32;
                case 64:
                    return core::Float64;
                default:
                    utility::LogError("Unsupported kDLFloat bits {}",
                                      dltype.bits);
            }
            break;
        default:
            utility::LogError("Unsupported dtype code {}", dltype.code);
    }
    return core::Undefined;
}
 
namespace {
// Adds version information for DLManagedTensorVersioned.
// This is a no-op for the other types.
template <class T>
void fillVersion(T& tensor) {}
 
template <>
void fillVersion<DLManagedTensorVersioned>(DLManagedTensorVersioned& tensor) {
    tensor.flags = 0;
    tensor.version.major = DLPACK_MAJOR_VERSION;
    tensor.version.minor = DLPACK_MINOR_VERSION;
}
 
}  // namespace
 
template <typename DLMT>
class Open3DDLManagedTensor {
private:
    Open3DDLManagedTensor(const Tensor& o3d_tensor) {
        o3d_tensor_ = o3d_tensor;
 
        // Prepare dl_device
        DLDevice dl_device = getDLPackDevice(o3d_tensor_);
 
        // Prepare dl_data_type
        DLDataType dl_data_type;
        Dtype dtype = o3d_tensor_.GetDtype();
 
        dl_data_type.code = DtypeToDLDataTypeCode(dtype);
        dl_data_type.bits = static_cast<uint8_t>(dtype.ByteSize() * 8);
        dl_data_type.lanes = 1;
 
        // Prepare dl_tensor, this uses dl_device_type, dl_device and
        // dl_data_type prepared above.
        DLTensor dl_tensor;
        // Not Blob's data pointer.
        dl_tensor.data = const_cast<void*>(o3d_tensor_.GetDataPtr());
        dl_tensor.device = dl_device;
        dl_tensor.ndim = static_cast<int>(o3d_tensor_.GetShape().size());
        dl_tensor.dtype = dl_data_type;
        // The shape pointer is alive for the lifetime of Open3DDLManagedTensor.
        dl_tensor.shape =
                const_cast<int64_t*>(o3d_tensor_.GetShapeRef().data());
        // The strides pointer is alive for the lifetime of
        // Open3DDLManagedTensor.
        dl_tensor.strides =
                const_cast<int64_t*>(o3d_tensor_.GetStridesRef().data());
        dl_tensor.byte_offset = 0;
 
        fillVersion(dl_managed_tensor_);
        dl_managed_tensor_.manager_ctx = this;
        dl_managed_tensor_.deleter = &Open3DDLManagedTensor::Deleter;
        dl_managed_tensor_.dl_tensor = dl_tensor;
    }
 
    Tensor o3d_tensor_;
    DLMT dl_managed_tensor_;
 
public:
    static DLMT* Create(const Tensor& o3d_tensor) {
        Open3DDLManagedTensor* o3d_dl_tensor =
                new Open3DDLManagedTensor(o3d_tensor);
        return &o3d_dl_tensor->dl_managed_tensor_;
    }
 
    static DLDevice getDLPackDevice(const Tensor& o3d_tensor) {
        // Prepare dl_device_type
        DLDeviceType dl_device_type;
        Device device = o3d_tensor.GetDevice();
        switch (device.GetType()) {
            case Device::DeviceType::CPU:
                dl_device_type = DLDeviceType::kDLCPU;
                break;
            case Device::DeviceType::CUDA:
                dl_device_type = DLDeviceType::kDLCUDA;
                break;
            case Device::DeviceType::SYCL:
                dl_device_type = DLDeviceType::kDLOneAPI;
                break;
            default:
                utility::LogError("ToDLPack: unsupported device type {}",
                                  device.ToString());
        }
        return DLDevice{dl_device_type, device.GetID()};
    }
 
    static void Deleter(DLMT* arg) {
        delete static_cast<Open3DDLManagedTensor*>(arg->manager_ctx);
    }
};
// Explicitly instantiate the template above for both classes.
template class Open3DDLManagedTensor<DLManagedTensor>;           // DLPack v0.x
template class Open3DDLManagedTensor<DLManagedTensorVersioned>;  // DLPack v1.x
 
struct Tensor::Iterator::Impl {
    Tensor* tensor_;
    int64_t index_;
    Tensor tensor_slice_;  // Stores temporary tensor slice with shared memory
                           // as the original tensor. This allows taking the &
                           // of the tensor for Iterator::operator->.
};
 
Tensor::Iterator::Iterator(pointer tensor, int64_t index)
    : impl_(std::make_unique<Impl>()) {
    impl_->tensor_ = tensor;
    impl_->index_ = index;
}
 
Tensor::Iterator::Iterator(const Tensor::Iterator& other)
    : impl_(std::make_unique<Impl>()) {
    impl_->tensor_ = other.impl_->tensor_;
    impl_->index_ = other.impl_->index_;
}
 
// Empty destructor since Impl is incomplete type in Tensor.h.
// https://stackoverflow.com/a/34073093/1255535
Tensor::Iterator::~Iterator() {}
 
Tensor::Iterator::reference Tensor::Iterator::operator*() const {
    return impl_->tensor_->operator[](impl_->index_);
}
 
Tensor::Iterator::pointer Tensor::Iterator::operator->() const {
    impl_->tensor_slice_ = impl_->tensor_->operator[](impl_->index_);
    return &impl_->tensor_slice_;
}
 
Tensor::Iterator& Tensor::Iterator::operator++() {
    impl_->index_++;
    return *this;
}
 
Tensor::Iterator Tensor::Iterator::operator++(int) {
    Iterator tmp(impl_->tensor_, impl_->index_);
    impl_->index_++;
    return tmp;
}
 
bool Tensor::Iterator::operator==(const Tensor::Iterator& other) const {
    return impl_->tensor_ == other.impl_->tensor_ &&
           impl_->index_ == other.impl_->index_;
}
 
bool Tensor::Iterator::operator!=(const Tensor::Iterator& other) const {
    return !(*this == other);
}
 
Tensor::Iterator Tensor::begin() {
    if (NumDims() == 0) {
        utility::LogError("Cannot iterate a scalar (0-dim) tensor.");
    }
    return Iterator(this, 0);
}
 
Tensor::Iterator Tensor::end() {
    if (NumDims() == 0) {
        utility::LogError("Cannot iterate a scalar (0-dim) tensor.");
    }
    return Iterator(this, shape_[0]);
}
 
struct Tensor::ConstIterator::Impl {
    const Tensor* tensor_;
    int64_t index_;
    Tensor tensor_slice_;  // Stores temporary tensor slice with shared memory
                           // as the original tensor. This allows taking the &
                           // of the tensor for ConstIterator::operator->.
};
 
Tensor::ConstIterator::ConstIterator(pointer tensor, int64_t index)
    : impl_(std::make_unique<Impl>()) {
    impl_->tensor_ = tensor;
    impl_->index_ = index;
}
 
Tensor::ConstIterator::ConstIterator(const Tensor::ConstIterator& other)
    : impl_(std::make_unique<Impl>()) {
    impl_->tensor_ = other.impl_->tensor_;
    impl_->index_ = other.impl_->index_;
}
 
// Empty destructor since Impl is incomplete type in Tensor.h.
// https://stackoverflow.com/a/34073093/1255535
Tensor::ConstIterator::~ConstIterator() {}
 
Tensor::ConstIterator::reference Tensor::ConstIterator::operator*() const {
    return impl_->tensor_->operator[](impl_->index_);
}
 
Tensor::ConstIterator::pointer Tensor::ConstIterator::operator->() const {
    impl_->tensor_slice_ = impl_->tensor_->operator[](impl_->index_);
    return &impl_->tensor_slice_;
}
 
Tensor::ConstIterator& Tensor::ConstIterator::operator++() {
    impl_->index_++;
    return *this;
}
 
Tensor::ConstIterator Tensor::ConstIterator::operator++(int) {
    ConstIterator tmp(impl_->tensor_, impl_->index_);
    impl_->index_++;
    return tmp;
}
 
bool Tensor::ConstIterator::operator==(
        const Tensor::ConstIterator& other) const {
    return impl_->tensor_ == other.impl_->tensor_ &&
           impl_->index_ == other.impl_->index_;
}
 
bool Tensor::ConstIterator::operator!=(
        const Tensor::ConstIterator& other) const {
    return !(*this == other);
}
 
Tensor::ConstIterator Tensor::cbegin() const {
    if (NumDims() == 0) {
        utility::LogError("Cannot iterate a scalar (0-dim) tensor.");
    }
    return ConstIterator(this, 0);
}
 
Tensor::ConstIterator Tensor::cend() const {
    if (NumDims() == 0) {
        utility::LogError("Cannot iterate a scalar (0-dim) tensor.");
    }
    return ConstIterator(this, shape_[0]);
}
 
// Equivalent to `Tensor& operator=(const Tensor& other) & = default;`.
// Manual implentaiton is need to avoid MSVC bug (error C2580:  multiple
// versions of a defaulted special member functions are not allowed.)
Tensor& Tensor::operator=(const Tensor& other) & {
    shape_ = other.shape_;
    strides_ = other.strides_;
    dtype_ = other.dtype_;
    blob_ = other.blob_;
    data_ptr_ = other.data_ptr_;
    return *this;
}
 
// Equivalent to `Tensor& operator=(Tensor&& other) & = default;`.
// Manual implentaiton is need to avoid MSVC bug (error C2580:  multiple
// versions of a defaulted special member functions are not allowed.)
Tensor& Tensor::operator=(Tensor&& other) & {
    shape_ = other.shape_;
    strides_ = other.strides_;
    dtype_ = other.dtype_;
    blob_ = other.blob_;
    data_ptr_ = other.data_ptr_;
    return *this;
}
 
Tensor& Tensor::operator=(const Tensor& other) && {
    kernel::Copy(other, *this);
    return *this;
}
 
Tensor& Tensor::operator=(Tensor&& other) && {
    kernel::Copy(other, *this);
    return *this;
}
 
Tensor Tensor::ReinterpretCast(const core::Dtype& dtype) const {
    if (dtype_.ByteSize() != dtype.ByteSize()) {
        utility::LogError(
                "Cannot reinterpret cast between data-types of different "
                "sizes. Expected data-type of {} bytes ({}), but got "
                "data-type {} of {} bytes.",
                dtype_.ByteSize(), dtype_.ToString(), dtype.ToString(),
                dtype.ByteSize());
    }
    return Tensor(shape_, strides_, data_ptr_, dtype, blob_);
}
 
Tensor Tensor::Empty(const SizeVector& shape,
                     Dtype dtype,
                     const Device& device) {
    return Tensor(shape, dtype, device);
}
 
Tensor Tensor::Zeros(const SizeVector& shape,
                     Dtype dtype,
                     const Device& device) {
    return Full(shape, 0, dtype, device);
}
 
Tensor Tensor::Ones(const SizeVector& shape,
                    Dtype dtype,
                    const Device& device) {
    return Full(shape, 1, dtype, device);
}
 
Tensor Tensor::Eye(int64_t n, Dtype dtype, const Device& device) {
    Tensor eye = Tensor::Zeros({n, n}, dtype, device);
    eye.AsStrided({n}, {eye.strides_[0] + eye.strides_[1]}).Fill(1);
    return eye;
}
 
Tensor Tensor::Diag(const Tensor& input) {
    const SizeVector& shape = input.GetShape();
    if (shape.size() != 1) {
        utility::LogError("Input tensor must be 1D, but got shape {}.",
                          input.shape_.ToString());
    }
    int64_t n = shape[0];
    Tensor diag = Tensor::Zeros({n, n}, input.GetDtype(), input.GetDevice());
    diag.AsStrided({n}, {diag.strides_[0] + diag.strides_[1]}) = input;
    return diag;
}
 
Tensor Tensor::Arange(const Scalar start,
                      const Scalar stop,
                      const Scalar step,
                      const Dtype dtype,
                      const Device& device) {
    start.AssertSameScalarType(stop,
                               "start must have the same scalar type as stop.");
    start.AssertSameScalarType(step,
                               "start must have the same scalar type as step.");
 
    if (step.Equal(0)) {
        utility::LogError("Step cannot be 0.");
    }
    if (stop.Equal(start)) {
        return Tensor({0}, dtype, device);
    }
 
    Tensor t_start;
    Tensor t_stop;
    Tensor t_step;
    DISPATCH_DTYPE_TO_TEMPLATE(dtype, [&]() {
        t_start = Tensor::Full({}, start.To<scalar_t>(), dtype, device);
        t_stop = Tensor::Full({}, stop.To<scalar_t>(), dtype, device);
        t_step = Tensor::Full({}, step.To<scalar_t>(), dtype, device);
    });
 
    return kernel::Arange(t_start, t_stop, t_step);
}
 
Tensor Tensor::Reverse() const {
    // TODO: Unoptimized with ai. Can be improved when negative step in Slice is
    // implemented.
    int64_t n = NumElements();
    Tensor reverse_idx = Tensor::Arange(n - 1, -1, -1);
    return View({n}).IndexGet({reverse_idx}).View(GetShape());
}
 
Tensor Tensor::GetItem(const TensorKey& tk) const {
    if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
        return IndexExtract(0, tk.GetIndex());
    } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
        if (NumDims() == 0) {
            utility::LogError("Cannot slice a scalar (0-dim) tensor.");
        }
        TensorKey tk_new = tk.InstantiateDimSize(shape_[0]);
        return Slice(0, tk_new.GetStart(), tk_new.GetStop(), tk_new.GetStep());
    } else if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
        return IndexGet({tk.GetIndexTensor()});
    } else {
        utility::LogError("Internal error: wrong TensorKeyMode.");
    }
}
 
Tensor Tensor::GetItem(const std::vector<TensorKey>& tks) const {
    if (std::any_of(tks.begin(), tks.end(), [](const TensorKey& tk) {
            return tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor;
        })) {
        // If tks contains one or more IndexTensor, the advanced indexing mode
        // is enabled. Under Advanced indexing mode, we do some preprocessing
        // with regular slicing, before sending to the advanced indexing engine.
        //
        // 1) TensorKey::Index: convert to a TensorKey::IndexTensor with the
        //    specified index.
        // 2) TensorKey::Slice: if the slice is non-full slice, slice the tensor
        //    first and then use full slice for the advanced indexing engine.
        //
        // e.g.
        // dst = src[1,     0:2,   [1, 2]]
        //           ^      ^      ^
        //           Index  Slice  IndexTensor
        // is done in two steps:
        // temp = src[:, 0:2, :]
        // dst = temp[[1], :, [1, 2]]
 
        std::vector<TensorKey> preprocess_tks;
 
        // Performs `temp = src[:, 0:2, :]`, see the example above.
        for (const TensorKey& tk : tks) {
            if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
                preprocess_tks.push_back(TensorKey::Slice(None, None, None));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
                preprocess_tks.push_back(tk);
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
                preprocess_tks.push_back(TensorKey::Slice(None, None, None));
            } else {
                utility::LogError("Internal error: wrong TensorKeyMode.");
            }
        }
        Tensor preprocess_t = GetItem(preprocess_tks);
 
        // Performs `dst = temp[[1], :, [1, 2]]`, see the example above.
        std::vector<Tensor> index_tensors;
        for (const TensorKey& tk : tks) {
            if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
                index_tensors.push_back(
                        Tensor(std::vector<int64_t>({tk.GetIndex()}), {1},
                               core::Int64, GetDevice()));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
                index_tensors.push_back(Tensor(std::vector<int64_t>{},
                                               core::Int64, GetDevice()));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
                index_tensors.push_back(tk.GetIndexTensor());
            } else {
                utility::LogError("Internal error: wrong TensorKeyMode.");
            }
        }
 
        // Calls Advanced indexing engine.
        return preprocess_t.IndexGet(index_tensors);
    }
 
    Tensor t = *this;
    int64_t slice_dim = 0;
    for (const TensorKey& tk : tks) {
        if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
            t = t.IndexExtract(slice_dim, tk.GetIndex());
        } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
            TensorKey tk_new = tk.InstantiateDimSize(t.shape_[slice_dim]);
            t = t.Slice(slice_dim, tk_new.GetStart(), tk_new.GetStop(),
                        tk_new.GetStep());
            slice_dim++;
        } else {
            utility::LogError("Internal error: wrong TensorKeyMode.");
        }
    }
    return t;
}
 
Tensor Tensor::SetItem(const Tensor& value) {
    this->AsRvalue() = value;
    return *this;
}
 
Tensor Tensor::SetItem(const TensorKey& tk, const Tensor& value) {
    if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
        IndexSet({tk.GetIndexTensor()}, value);
    } else {
        this->GetItem(tk) = value;
    }
    return *this;
}
 
Tensor Tensor::SetItem(const std::vector<TensorKey>& tks, const Tensor& value) {
    if (std::any_of(tks.begin(), tks.end(), [](const TensorKey& tk) {
            return tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor;
        })) {
        // Advanced indexing mode, see Tensor::GetItem for detailed docs.
        std::vector<TensorKey> preprocess_tks;
 
        for (const TensorKey& tk : tks) {
            if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
                preprocess_tks.push_back(TensorKey::Slice(None, None, None));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
                preprocess_tks.push_back(tk);
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
                preprocess_tks.push_back(TensorKey::Slice(None, None, None));
            } else {
                utility::LogError("Internal error: wrong TensorKeyMode.");
            }
        }
        Tensor preprocess_t = GetItem(preprocess_tks);
 
        std::vector<Tensor> index_tensors;
        for (const TensorKey& tk : tks) {
            if (tk.GetMode() == TensorKey::TensorKeyMode::Index) {
                index_tensors.push_back(
                        Tensor(std::vector<int64_t>({tk.GetIndex()}), {1},
                               core::Int64, GetDevice()));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::Slice) {
                index_tensors.push_back(Tensor(std::vector<int64_t>{},
                                               core::Int64, GetDevice()));
            } else if (tk.GetMode() == TensorKey::TensorKeyMode::IndexTensor) {
                index_tensors.push_back(tk.GetIndexTensor());
            } else {
                utility::LogError("Internal error: wrong TensorKeyMode.");
            }
        }
 
        preprocess_t.IndexSet(index_tensors, value);
    } else {
        this->GetItem(tks) = value;
    }
 
    return *this;
}
 
Tensor Tensor::Append(const Tensor& other,
                      const utility::optional<int64_t>& axis) const {
    return core::Append(*this, other, axis);
}
 
Tensor Tensor::Broadcast(const SizeVector& dst_shape) const {
    if (!shape_util::CanBeBrocastedToShape(shape_, dst_shape)) {
        utility::LogError("Cannot broadcast shape {} to shape {}.",
                          shape_.ToString(), dst_shape);
    }
    Tensor dst_tensor(dst_shape, dtype_, GetDevice());
    dst_tensor.AsRvalue() = *this;
    return dst_tensor;
}
 
Tensor Tensor::Expand(const SizeVector& dst_shape) const {
    if (!shape_util::CanBeBrocastedToShape(shape_, dst_shape)) {
        utility::LogError("Cannot expand shape {} to shape {}.",
                          shape_.ToString(), dst_shape);
    }
    int64_t src_ndims = NumDims();
    int64_t dst_ndims = dst_shape.size();
    int64_t omitted_ndims = dst_ndims - src_ndims;
 
    // Fill 1 in shape for omitted dimensions in front.
    // Noe that unexpanded_new_shape is not the expanded shape. The expanded
    // shape is the dst_shape.
    SizeVector unexpanded_new_shape(dst_ndims, 1);
    for (int64_t i = 0; i < src_ndims; ++i) {
        unexpanded_new_shape[i + omitted_ndims] = shape_[i];
    }
 
    // Fill 0 in strides for omitted dimensions in front.
    SizeVector new_strides(dst_ndims, 0);
    for (int64_t i = 0; i < src_ndims; ++i) {
        new_strides[i + omitted_ndims] = strides_[i];
    }
 
    // Set stride to 0 if the dimension is expanded.
    for (int64_t i = 0; i < dst_ndims; ++i) {
        if (unexpanded_new_shape[i] == 1 && dst_shape[i] != 1) {
            new_strides[i] = 0;
        }
    }
 
    return AsStrided(dst_shape, new_strides);
}
 
Tensor Tensor::Reshape(const SizeVector& dst_shape) const {
    SizeVector inferred_dst_shape =
            shape_util::InferShape(dst_shape, NumElements());
    bool can_restride;
    SizeVector new_strides;
    std::tie(can_restride, new_strides) =
            shape_util::Restride(shape_, strides_, inferred_dst_shape);
    if (can_restride) {
        return AsStrided(inferred_dst_shape, new_strides);
    } else {
        return Contiguous().View(inferred_dst_shape);
    }
}
 
Tensor Tensor::Flatten(int64_t start_dim /*= 0*/,
                       int64_t end_dim /*= -1*/) const {
    int64_t num_dims = NumDims();
    if (num_dims == 0) {
        // Flattening a 0-d tensor is equivalent to flattening the tensor
        // reshaped to 1-d. Technically, we cannot have a start_dim or end_dim,
        // since a 0-d tensor cannot be indexed, e.g. np.array(100)[0] is not
        // valid. But start_dim = 0 and end_dim = -1 are the default parameter
        // values so we make an exception case for 0-d. We reshape it to 1-d for
        // boundary checks of start_dim and end_dim.
        return Reshape({1}).Flatten(start_dim, end_dim);
    }
    core::SizeVector shape = GetShape();
    core::SizeVector dst_shape;
    start_dim = shape_util::WrapDim(start_dim, num_dims, false);
    end_dim = shape_util::WrapDim(end_dim, num_dims, false);
    if (end_dim < start_dim) {
        utility::LogError(
                "start_dim {} must be smaller or equal to end_dim {}.",
                start_dim, end_dim);
    }
    // Multiply the flattened dimensions together.
    int64_t flat_dimension_size = 1;
    for (int64_t dim = 0; dim < num_dims; dim++) {
        if (dim >= start_dim && dim <= end_dim) {
            flat_dimension_size *= shape[dim];
            if (dim == end_dim) {
                dst_shape.push_back(flat_dimension_size);
            }
        } else {
            dst_shape.push_back(shape[dim]);
        }
    }
    return Reshape(dst_shape);
}
 
Tensor Tensor::View(const SizeVector& dst_shape) const {
    SizeVector inferred_dst_shape =
            shape_util::InferShape(dst_shape, NumElements());
    bool can_restride;
    SizeVector new_strides;
    std::tie(can_restride, new_strides) =
            shape_util::Restride(shape_, strides_, inferred_dst_shape);
    if (can_restride) {
        return AsStrided(inferred_dst_shape, new_strides);
    } else {
        utility::LogError(
                "View shape {} is not compatible with Tensor's size {} and "
                "sride {}, at least one dimension spacs across two contiguous "
                "subspaces. Use Reshape() instead.",
                dst_shape, shape_, strides_);
    }
}
 
Tensor Tensor::To(Dtype dtype, bool copy /*= false*/) const {
    if (!copy && dtype_ == dtype) {
        return *this;
    }
    // We only support scalar type conversion.
    if (dtype_.IsObject() || dtype.IsObject()) {
        utility::LogError("Cannot cast type from {} to {}.", dtype_.ToString(),
                          dtype.ToString());
    }
    Tensor dst_tensor(shape_, dtype, GetDevice());
    kernel::Copy(*this, dst_tensor);
    return dst_tensor;
}
 
Tensor Tensor::To(const Device& device, bool copy /*= false*/) const {
    if (!copy && GetDevice() == device) {
        return *this;
    }
    Tensor dst_tensor(shape_, dtype_, device);
    kernel::Copy(*this, dst_tensor);
    return dst_tensor;
}
 
Tensor Tensor::To(const Device& device,
                  Dtype dtype,
                  bool copy /*= false*/) const {
    Tensor dst_tensor = To(dtype, copy);
    dst_tensor = dst_tensor.To(device, copy);
    return dst_tensor;
}
 
void Tensor::CopyFrom(const Tensor& other) { AsRvalue() = other; }
 
Tensor Tensor::Contiguous() const {
    if (IsContiguous()) {
        return *this;
    } else {
        return To(GetDevice(), /*copy=*/true);
    }
}
 
std::string Tensor::ToString(bool with_suffix,
                             const std::string& indent) const {
    std::ostringstream rc;
    if (IsCUDA() || IsSYCL() || !IsContiguous()) {
        Tensor host_contiguous_tensor = Contiguous().To(Device("CPU:0"));
        rc << host_contiguous_tensor.ToString(false, indent);
    } else {
        if (shape_.NumElements() == 0) {
            rc << indent;
            rc << "0-element Tensor";
        } else if (shape_.size() == 0) {
            rc << indent;
            rc << ScalarPtrToString(data_ptr_);
        } else if (shape_.size() == 1) {
            const char* ptr = static_cast<const char*>(data_ptr_);
            rc << "[";
            std::string delim = "";
            int64_t element_byte_size = dtype_.ByteSize();
            for (int64_t i = 0; i < shape_.NumElements(); ++i) {
                rc << delim << ScalarPtrToString(ptr);
                delim = " ";
                ptr += element_byte_size;
            }
            rc << "]";
        } else {
            rc << "[";
            std::string delim = "";
            std::string child_indent = "";
            for (int64_t i = 0; i < shape_[0]; ++i) {
                rc << delim << child_indent
                   << this->operator[](i).ToString(false, indent + " ");
                delim = ",\n";
                child_indent = indent + " ";
            }
            rc << "]";
        }
    }
    if (with_suffix) {
        rc << fmt::format("\nTensor[shape={}, stride={}, {}, {}, {}]",
                          shape_.ToString(), strides_.ToString(),
                          dtype_.ToString(), GetDevice().ToString(), data_ptr_);
    }
    return rc.str();
}
 
std::string Tensor::ScalarPtrToString(const void* ptr) const {
    std::string str = "";
    if (dtype_ == core::Bool) {
        str = *static_cast<const unsigned char*>(ptr) ? "True" : "False";
    } else if (dtype_.IsObject()) {
        str = fmt::format("{}", fmt::ptr(ptr));
    } else {
        DISPATCH_DTYPE_TO_TEMPLATE(dtype_, [&]() {
            str = fmt::format("{}", *static_cast<const scalar_t*>(ptr));
        });
    }
    return str;
}
 
Tensor Tensor::operator[](int64_t i) const { return IndexExtract(0, i); }
 
Tensor Tensor::IndexExtract(int64_t dim, int64_t idx) const {
    if (shape_.size() == 0) {
        utility::LogError("Tensor has shape (), cannot be indexed.");
    }
    dim = shape_util::WrapDim(dim, NumDims());
    idx = shape_util::WrapDim(idx, shape_[dim]);
 
    SizeVector new_shape(shape_);
    new_shape.erase(new_shape.begin() + dim);
    SizeVector new_strides(strides_);
    new_strides.erase(new_strides.begin() + dim);
    void* new_data_ptr = static_cast<char*>(data_ptr_) +
                         strides_[dim] * dtype_.ByteSize() * idx;
    return Tensor(new_shape, new_strides, new_data_ptr, dtype_, blob_);
}
 
Tensor Tensor::Slice(int64_t dim,
                     int64_t start,
                     int64_t stop,
                     int64_t step) const {
    if (shape_.size() == 0) {
        utility::LogError("Slice cannot be applied to 0-dim Tensor.");
    }
    dim = shape_util::WrapDim(dim, NumDims());
    if (dim < 0 || dim >= static_cast<int64_t>(shape_.size())) {
        utility::LogError("Dim {} is out of bound for SizeVector of length {}.",
                          dim, shape_.size());
    }
    if (step == 0) {
        utility::LogError("Step size cannot be 0.");
    } else if (step < 0) {
        // TODO: support negative step sizes
        utility::LogError("Step size cannot be < 0.");
    }
 
    // Wrap start. Out-of-range slice is valid and produces empty Tensor.
    if (start < 0) {
        start += shape_[dim];
    }
    if (start < 0) {
        start = 0;
    } else if (start >= shape_[dim]) {
        start = shape_[dim];
    }
 
    // Wrap stop. Out-of-range slice is valid and produces empty Tensor.
    if (stop < 0) {
        stop += shape_[dim];
    }
    if (stop < start) {
        stop = start;
    } else if (stop >= shape_[dim]) {
        stop = shape_[dim];
    }
 
    void* new_data_ptr = static_cast<char*>(data_ptr_) +
                         start * strides_[dim] * dtype_.ByteSize();
    SizeVector new_shape = shape_;
    SizeVector new_strides = strides_;
    new_shape[dim] = (stop - start + step - 1) / step;
    new_strides[dim] = strides_[dim] * step;
    return Tensor(new_shape, new_strides, new_data_ptr, dtype_, blob_);
}
 
Tensor Tensor::IndexGet(const std::vector<Tensor>& index_tensors) const {
    if (NumDims() == 0) {
        if (index_tensors.size() != 1) {
            utility::LogError(
                    "A 0-D tensor can only be indexed by a 0-D boolean tensor, "
                    "but got {} index tensors.",
                    index_tensors.size());
        }
        Tensor index_tensor = index_tensors[0];
        core::AssertTensorShape(index_tensor, {});
        core::AssertTensorDtype(index_tensor, core::Bool);
 
        if (index_tensor.IsNonZero()) {
            // E.g. np.array(5)[np.array(True)].
            return Clone();
        } else {
            // E.g. np.array(5)[np.array(False)].
            // The output tensor becomes 1D of 0 element.
            return Tensor(/*shape=*/{0}, GetDtype(), GetDevice());
        }
    }
 
    AdvancedIndexPreprocessor aip(*this, index_tensors);
    Tensor dst = Tensor(aip.GetOutputShape(), dtype_, GetDevice());
 
    kernel::IndexGet(aip.GetTensor(), dst, aip.GetIndexTensors(),
                     aip.GetIndexedShape(), aip.GetIndexedStrides());
 
    return dst;
}
 
void Tensor::IndexSet(const std::vector<Tensor>& index_tensors,
                      const Tensor& src_tensor) {
    if (NumDims() == 0) {
        if (index_tensors.size() != 1) {
            utility::LogError(
                    "A 0-D tensor can only be indexed by a 0-D boolean tensor, "
                    "but got {} index tensors.",
                    index_tensors.size());
        }
        Tensor index_tensor = index_tensors[0];
        core::AssertTensorShape(index_tensor, {});
        core::AssertTensorDtype(index_tensor, core::Bool);
 
        // Example index set
        // t = np.array(5)
        // t[np.array(True)]  = 10                 // Works, assigned
        // t[np.array(True)]  = np.array(10)       // Works, assigned
        // t[np.array(True)]  = np.array([10])     // Works, assigned
        // t[np.array(True)]  = np.array([[10]])   // Cannot assign 2D
        // t[np.array(True)]  = np.array([10, 11]) // Cannot assign 1+ values
        // t[np.array(False)] = 10                 // Works, unchanged
        // t[np.array(False)] = np.array(10)       // Works, unchanged
        // t[np.array(False)] = np.array([10])     // Works, unchanged
        // t[np.array(False)] = np.array([[10]])   // Cannot assign 2D
        // t[np.array(False)] = np.array([10, 11]) // Cannot assign 1+ values
 
        // Assert 0-D or 1-D.
        if (src_tensor.NumDims() > 1) {
            utility::LogError(
                    "Boolean indexing of a 0-D tensor can only be assigned "
                    "with 0 or 1-dimensional input, but got {} dimensions.",
                    src_tensor.NumDims());
        }
        // Assert single element.
        if (src_tensor.NumElements() != 1) {
            utility::LogError(
                    "Boolean indexing of a 0-D tensor can only be assigned "
                    "with input containing 1 element, but got {} elements.",
                    src_tensor.NumElements());
        }
        if (index_tensors[0].IsNonZero()) {
            DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(src_tensor.GetDtype(), [&]() {
                AsRvalue() = src_tensor.Item<scalar_t>();
            });
        }
        return;
    }
 
    AdvancedIndexPreprocessor aip(*this, index_tensors);
    Tensor pre_processed_dst = aip.GetTensor();
 
    kernel::IndexSet(src_tensor, pre_processed_dst, aip.GetIndexTensors(),
                     aip.GetIndexedShape(), aip.GetIndexedStrides());
}
 
void Tensor::IndexAdd_(int64_t dim, const Tensor& index, const Tensor& src) {
    if (index.NumDims() != 1) {
        utility::LogError("IndexAdd_ only supports 1D index tensors.");
    }
 
    // Dim check.
    if (dim < 0) {
        utility::LogError("IndexAdd_ only supports sum at non-negative dim.");
    }
    if (NumDims() <= dim) {
        utility::LogError("Sum dim {} exceeds tensor dim {}.", dim, NumDims());
    }
 
    // shape check
    if (src.NumDims() != NumDims()) {
        utility::LogError(
                "IndexAdd_ only supports src tensor with same dimension as "
                "this tensor.");
    }
    for (int64_t d = 0; d < NumDims(); ++d) {
        if (d != dim && src.GetShape(d) != GetShape(d)) {
            utility::LogError(
                    "IndexAdd_ only supports src tensor with same shape as "
                    "this "
                    "tensor except dim {}.",
                    dim);
        }
    }
 
    // Type check.
    AssertTensorDtype(index, core::Int64);
    AssertTensorDtype(*this, src.GetDtype());
 
    // Apply kernel.
    kernel::IndexAdd_(dim, index, src, *this);
}
 
Tensor Tensor::Permute(const SizeVector& dims) const {
    // Check dimension size
    if (static_cast<int64_t>(dims.size()) != NumDims()) {
        utility::LogError(
                "Tensor has {} dimensions, but permuntation have {} "
                "dimensions.",
                NumDims(), dims.size());
    }
    int64_t n_dims = NumDims();
 
    // Check dims are permuntation of [0, 1, 2, ..., n-1]
    std::vector<bool> seen_dims(n_dims, false);
    for (const int64_t& dim : dims) {
        seen_dims[shape_util::WrapDim(dim, n_dims)] = true;
    }
    if (!std::all_of(seen_dims.begin(), seen_dims.end(),
                     [](bool seen) { return seen; })) {
        utility::LogError("Permute dims must be a permuntation from 0 to {}",
                          dims.size() - 1);
    }
 
    // Map to new shape and strides
    SizeVector new_shape(n_dims);
    SizeVector new_strides(n_dims);
    for (int64_t i = 0; i < n_dims; ++i) {
        int64_t old_dim = shape_util::WrapDim(dims[i], n_dims);
        new_shape[i] = shape_[old_dim];
        new_strides[i] = strides_[old_dim];
    }
 
    return AsStrided(new_shape, new_strides);
}
 
Tensor Tensor::AsStrided(const SizeVector& new_shape,
                         const SizeVector& new_strides) const {
    Tensor result(new_shape, new_strides, const_cast<void*>(data_ptr_), dtype_,
                  blob_);
    return result;
}
 
Tensor Tensor::Transpose(int64_t dim0, int64_t dim1) const {
    int64_t n_dims = NumDims();
    dim0 = shape_util::WrapDim(dim0, n_dims);
    dim1 = shape_util::WrapDim(dim1, n_dims);
    SizeVector dims(n_dims);
    std::iota(dims.begin(), dims.end(), 0);
    dims[dim0] = dim1;
    dims[dim1] = dim0;
    return Permute(dims);
}
 
Tensor Tensor::T() const {
    int64_t n_dims = NumDims();
    if (n_dims <= 1) {
        return *this;
    } else if (n_dims == 2) {
        return Transpose(0, 1);
    } else {
        utility::LogError(
                "Tensor::T() expects a Tensor with <= 2 dimensions, but the "
                "Tensor as {} dimensions.");
    }
}
 
double Tensor::Det() const {
    AssertTensorDtypes(*this, {Float32, Float64});
    return core::Det(*this);
}
 
Tensor Tensor::Add(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      dtype_, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Add);
 
    return dst_tensor;
}
 
Tensor Tensor::Add(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = Add(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Add_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Add);
 
    return *this;
}
 
Tensor Tensor::Add_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Add_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Sub(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      dtype_, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Sub);
 
    return dst_tensor;
}
 
Tensor Tensor::Sub(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = Sub(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Sub_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Sub);
 
    return *this;
}
 
Tensor Tensor::Sub_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Sub_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Mul(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      dtype_, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Mul);
 
    return dst_tensor;
}
 
Tensor Tensor::Mul(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = Mul(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Mul_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Mul);
 
    return *this;
}
 
Tensor Tensor::Mul_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Mul_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Div(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      dtype_, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Div);
 
    return dst_tensor;
}
 
Tensor Tensor::Div(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = Div(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Div_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
    AssertTensorDtype(value, GetDtype());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Div);
    return *this;
}
 
Tensor Tensor::Div_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Div_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Sum(const SizeVector& dims, bool keepdim) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, keepdim), dtype_,
               GetDevice());
    kernel::Reduction(*this, dst, dims, keepdim, kernel::ReductionOpCode::Sum);
    return dst;
}
 
Tensor Tensor::Mean(const SizeVector& dims, bool keepdim) const {
    AssertTensorDtypes(*this, {Float32, Float64});
 
    // Following Numpy's semantics, reduction on 0-sized Tensor will result in
    // NaNs and a warning. A straightforward method is used now. Later it can be
    // extended to handle overflow and underflow in a better way.
    if (NumElements() == 0) {
        utility::LogWarning("Computing mean of 0-sized Tensor.");
    }
    Tensor sum = Sum(dims, keepdim);
    double factor = static_cast<double>(sum.NumElements()) / NumElements();
    return sum * factor;
}
 
Tensor Tensor::Prod(const SizeVector& dims, bool keepdim) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, keepdim), dtype_,
               GetDevice());
    kernel::Reduction(*this, dst, dims, keepdim, kernel::ReductionOpCode::Prod);
    return dst;
}
 
Tensor Tensor::Min(const SizeVector& dims, bool keepdim) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, keepdim), dtype_,
               GetDevice());
    kernel::Reduction(*this, dst, dims, keepdim, kernel::ReductionOpCode::Min);
    return dst;
}
 
Tensor Tensor::Max(const SizeVector& dims, bool keepdim) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, keepdim), dtype_,
               GetDevice());
    kernel::Reduction(*this, dst, dims, keepdim, kernel::ReductionOpCode::Max);
    return dst;
}
 
Tensor Tensor::ArgMin(const SizeVector& dims) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, false), core::Int64,
               GetDevice());
    kernel::Reduction(*this, dst, dims, false, kernel::ReductionOpCode::ArgMin);
    return dst;
}
 
Tensor Tensor::ArgMax(const SizeVector& dims) const {
    Tensor dst(shape_util::ReductionShape(shape_, dims, false), core::Int64,
               GetDevice());
    kernel::Reduction(*this, dst, dims, false, kernel::ReductionOpCode::ArgMax);
    return dst;
}
 
Tensor Tensor::Sqrt() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Sqrt);
    return dst_tensor;
}
 
Tensor Tensor::Sqrt_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Sqrt);
    return *this;
}
 
Tensor Tensor::Sin() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Sin);
    return dst_tensor;
}
 
Tensor Tensor::Sin_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Sin);
    return *this;
}
 
Tensor Tensor::Cos() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Cos);
    return dst_tensor;
}
 
Tensor Tensor::Cos_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Cos);
    return *this;
}
 
Tensor Tensor::Neg() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Neg);
    return dst_tensor;
}
 
Tensor Tensor::Neg_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Neg);
    return *this;
}
 
Tensor Tensor::Exp() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Exp);
    return dst_tensor;
}
 
Tensor Tensor::Exp_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Exp);
    return *this;
}
 
Tensor Tensor::Abs() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Abs);
    return dst_tensor;
}
 
Tensor Tensor::Abs_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::Abs);
    return *this;
}
 
Tensor Tensor::IsNan() const {
    if (dtype_ == core::Float32 || dtype_ == core::Float64) {
        Tensor dst_tensor(shape_, core::Bool, GetDevice());
        kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::IsNan);
        return dst_tensor;
    } else {
        return Tensor::Zeros(shape_, core::Bool, GetDevice());
    }
}
 
Tensor Tensor::IsInf() const {
    if (dtype_ == core::Float32 || dtype_ == core::Float64) {
        Tensor dst_tensor(shape_, core::Bool, GetDevice());
        kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::IsInf);
        return dst_tensor;
    } else {
        return Tensor::Zeros(shape_, core::Bool, GetDevice());
    }
}
 
Tensor Tensor::IsFinite() const {
    if (dtype_ == core::Float32 || dtype_ == core::Float64) {
        Tensor dst_tensor(shape_, core::Bool, GetDevice());
        kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::IsFinite);
        return dst_tensor;
    } else {
        return Tensor::Ones(shape_, core::Bool, GetDevice());
    }
}
 
Tensor Tensor::Clip(Scalar min_val, Scalar max_val) const {
    Tensor dst_tensor = this->Clone();
    return dst_tensor.Clip_(min_val, max_val);
}
 
// TODO: Implement with kernel.
Tensor Tensor::Clip_(Scalar min_val, Scalar max_val) {
    DISPATCH_DTYPE_TO_TEMPLATE(dtype_, [&]() {
        scalar_t min_val_casted = min_val.To<scalar_t>();
        this->SetItem(TensorKey::IndexTensor(this->Lt(min_val_casted)),
                      Full({}, min_val_casted, dtype_, GetDevice()));
 
        scalar_t max_val_casted = max_val.To<scalar_t>();
        this->SetItem(TensorKey::IndexTensor(this->Gt(max_val_casted)),
                      Full({}, max_val_casted, dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Floor() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Floor);
    return dst_tensor;
}
 
Tensor Tensor::Ceil() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Ceil);
    return dst_tensor;
}
 
Tensor Tensor::Round() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Round);
    return dst_tensor;
}
 
Tensor Tensor::Trunc() const {
    Tensor dst_tensor(shape_, dtype_, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::Trunc);
    return dst_tensor;
}
 
Device Tensor::GetDevice() const {
    if (blob_ == nullptr) {
        utility::LogError("Blob is null, cannot get device");
    }
    return blob_->GetDevice();
}
 
Tensor Tensor::LogicalNot() const {
    Tensor dst_tensor(shape_, core::Bool, GetDevice());
    kernel::UnaryEW(*this, dst_tensor, kernel::UnaryEWOpCode::LogicalNot);
    return dst_tensor;
}
 
Tensor Tensor::LogicalNot_() {
    kernel::UnaryEW(*this, *this, kernel::UnaryEWOpCode::LogicalNot);
    return *this;
}
 
Tensor Tensor::LogicalAnd(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor,
                     kernel::BinaryEWOpCode::LogicalAnd);
    return dst_tensor;
}
 
Tensor Tensor::LogicalAnd(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = LogicalAnd(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::LogicalAnd_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::LogicalAnd);
    return *this;
}
 
Tensor Tensor::LogicalAnd_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        LogicalAnd_(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::LogicalOr(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor,
                     kernel::BinaryEWOpCode::LogicalOr);
    return dst_tensor;
}
 
Tensor Tensor::LogicalOr(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = LogicalOr(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::LogicalOr_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::LogicalOr);
    return *this;
}
 
Tensor Tensor::LogicalOr_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        LogicalOr_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::LogicalXor(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor,
                     kernel::BinaryEWOpCode::LogicalXor);
    return dst_tensor;
}
 
Tensor Tensor::LogicalXor(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor = LogicalXor(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::LogicalXor_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::LogicalXor);
    return *this;
}
 
Tensor Tensor::LogicalXor_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        LogicalXor_(
                Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Gt(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Gt);
    return dst_tensor;
}
 
Tensor Tensor::Gt(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Gt(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Gt_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Gt);
    return *this;
}
 
Tensor Tensor::Gt_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Gt_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Lt(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Lt);
    return dst_tensor;
}
 
Tensor Tensor::Lt(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Lt(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Lt_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Lt);
    return *this;
}
 
Tensor Tensor::Lt_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Lt_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Ge(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Ge);
    return dst_tensor;
}
 
Tensor Tensor::Ge(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Ge(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Ge_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Ge);
    return *this;
}
 
Tensor Tensor::Ge_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Ge_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Le(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Le);
    return dst_tensor;
}
 
Tensor Tensor::Le(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Le(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Le_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Le);
    return *this;
}
 
Tensor Tensor::Le_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Le_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Eq(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Eq);
    return dst_tensor;
}
 
Tensor Tensor::Eq(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Eq(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Eq_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Eq);
    return *this;
}
 
Tensor Tensor::Eq_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Eq_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
Tensor Tensor::Ne(const Tensor& value) const {
    AssertTensorDevice(value, GetDevice());
 
    Tensor dst_tensor(shape_util::BroadcastedShape(shape_, value.shape_),
                      core::Bool, GetDevice());
    kernel::BinaryEW(*this, value, dst_tensor, kernel::BinaryEWOpCode::Ne);
    return dst_tensor;
}
 
Tensor Tensor::Ne(Scalar value) const {
    Tensor dst_tensor;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        dst_tensor =
                Ne(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return dst_tensor;
}
 
Tensor Tensor::Ne_(const Tensor& value) {
    AssertTensorDevice(value, GetDevice());
 
    kernel::BinaryEW(*this, value, *this, kernel::BinaryEWOpCode::Ne);
    return *this;
}
 
Tensor Tensor::Ne_(Scalar value) {
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        Ne_(Tensor::Full({}, value.To<scalar_t>(), dtype_, GetDevice()));
    });
    return *this;
}
 
std::vector<Tensor> Tensor::NonZeroNumpy() const {
    Tensor result = kernel::NonZero(*this);
    std::vector<Tensor> results;
    for (int64_t dim = 0; dim < NumDims(); dim++) {
        results.push_back(result[dim].Clone());
    }
    return results;
}
 
Tensor Tensor::NonZero() const { return kernel::NonZero(*this); }
 
bool Tensor::IsNonZero() const {
    if (shape_.NumElements() != 1) {
        utility::LogError(
                "Tensor must have exactly one element to be evaluated as "
                "boolean.");
    }
    bool rc = false;
    DISPATCH_DTYPE_TO_TEMPLATE_WITH_BOOL(dtype_, [&]() {
        rc = Item<scalar_t>() != static_cast<scalar_t>(0);
    });
    return rc;
}
 
Tensor Tensor::All(const utility::optional<SizeVector>& dims,
                   bool keepdim) const {
    AssertTensorDtype(*this, core::Bool);
 
    Tensor dst;
    if (dims.has_value()) {
        dst = Tensor(shape_util::ReductionShape(shape_, dims.value(), keepdim),
                     dtype_, GetDevice());
        kernel::Reduction(*this, dst, dims.value(), keepdim,
                          kernel::ReductionOpCode::All);
    } else {
        dst = Tensor({}, dtype_, GetDevice());
        kernel::Reduction(*this, dst, shape_util::Iota(NumDims()), false,
                          kernel::ReductionOpCode::All);
    }
 
    return dst;
}
 
Tensor Tensor::Any(const utility::optional<SizeVector>& dims,
                   bool keepdim) const {
    AssertTensorDtype(*this, core::Bool);
 
    Tensor dst;
    if (dims.has_value()) {
        dst = Tensor(shape_util::ReductionShape(shape_, dims.value(), keepdim),
                     dtype_, GetDevice());
        kernel::Reduction(*this, dst, dims.value(), keepdim,
                          kernel::ReductionOpCode::Any);
    } else {
        dst = Tensor({}, dtype_, GetDevice());
        kernel::Reduction(*this, dst, shape_util::Iota(NumDims()), false,
                          kernel::ReductionOpCode::Any);
    }
 
    return dst;
}
 
DLManagedTensor* Tensor::ToDLPack() const {
    return Open3DDLManagedTensor<DLManagedTensor>::Create(*this);
}
DLManagedTensorVersioned* Tensor::ToDLPackVersioned() const {
    return Open3DDLManagedTensor<DLManagedTensorVersioned>::Create(*this);
}
 
namespace {
template <typename DLMT>
Tensor FromDLPackImpl(const DLMT* src, std::function<void(void*)> deleter) {
    Device src_device;
    switch (src->dl_tensor.device.device_type) {
        case DLDeviceType::kDLCPU:
            src_device = Device("CPU", src->dl_tensor.device.device_id);
            break;
        case DLDeviceType::kDLCUDA:
            src_device = Device("CUDA", src->dl_tensor.device.device_id);
            break;
        case DLDeviceType::kDLOneAPI:
            src_device = Device("SYCL", src->dl_tensor.device.device_id);
            break;
        default:
            utility::LogError(
                    "Unsupported device_type {}",
                    static_cast<int>(src->dl_tensor.device.device_type));
    }
 
    Dtype dtype = DLDataTypeToDtype(src->dl_tensor.dtype);
 
    // Open3D Blob's expects an std::function<void(void*)> deleter.
    if (!deleter) {
        deleter = [src](void* dummy) -> void {
            if (src->deleter != nullptr) {
                src->deleter(const_cast<DLMT*>(src));
            }
        };
    }
 
    SizeVector shape(src->dl_tensor.shape,
                     src->dl_tensor.shape + src->dl_tensor.ndim);
 
    SizeVector strides;
    if (!src->dl_tensor.strides) {  // default row major contiguous strides
        strides = shape_util::DefaultStrides(shape);
    } else {
        strides = SizeVector(src->dl_tensor.strides,
                             src->dl_tensor.strides + src->dl_tensor.ndim);
    }
 
    auto blob =
            std::make_shared<Blob>(src_device, src->dl_tensor.data, deleter);
 
    // src->dl_tensor.byte_offset is ignored in PyTorch and MXNet, but
    // according to dlpack.h, we added the offset here.
    return Tensor(shape, strides,
                  reinterpret_cast<char*>(blob->GetDataPtr()) +
                          src->dl_tensor.byte_offset,
                  dtype, blob);
}
}  // namespace
Tensor Tensor::FromDLPack(const DLManagedTensor* src,
                          std::function<void(void*)> deleter) {
    return FromDLPackImpl<DLManagedTensor>(src, std::move(deleter));
}
Tensor Tensor::FromDLPackVersioned(const DLManagedTensorVersioned* src,
                                   std::function<void(void*)> deleter) {
    return FromDLPackImpl<DLManagedTensorVersioned>(src, std::move(deleter));
}
 
void Tensor::Save(const std::string& file_name) const {
    t::io::WriteNpy(file_name, *this);
}
 
Tensor Tensor::Load(const std::string& file_name) {
    return t::io::ReadNpy(file_name);
}
 
bool Tensor::AllEqual(const Tensor& other) const {
    AssertTensorDevice(other, GetDevice());
    AssertTensorDtype(other, GetDtype());
 
    if (shape_ != other.shape_) {
        return false;
    }
    return (*this == other).All().Item<bool>();
}
 
bool Tensor::AllClose(const Tensor& other, double rtol, double atol) const {
    // TODO: support nan;
    return IsClose(other, rtol, atol).All().Item<bool>();
}
 
Tensor Tensor::IsClose(const Tensor& other, double rtol, double atol) const {
    AssertTensorDevice(other, GetDevice());
    AssertTensorDtype(other, GetDtype());
    AssertTensorShape(other, GetShape());
 
    Tensor lhs = this->To(core::Float64);
    Tensor rhs = other.To(core::Float64);
    Tensor actual_error = (lhs - rhs).Abs();
    Tensor max_error = atol + rtol * rhs.Abs();
    return actual_error <= max_error;
}
 
bool Tensor::IsSame(const Tensor& other) const {
    AssertTensorDevice(other, GetDevice());
    return blob_ == other.blob_ && shape_ == other.shape_ &&
           strides_ == other.strides_ && data_ptr_ == other.data_ptr_ &&
           dtype_ == other.dtype_;
}
 
Tensor Tensor::Matmul(const Tensor& rhs) const {
    AssertTensorDevice(rhs, GetDevice());
    AssertTensorDtype(rhs, GetDtype());
 
    Tensor output;
    core::Matmul(*this, rhs, output);
    return output;
}
 
Tensor Tensor::Solve(const Tensor& rhs) const {
    AssertTensorDtypes(*this, {Float32, Float64});
    AssertTensorDevice(rhs, GetDevice());
    AssertTensorDtype(rhs, GetDtype());
 
    Tensor output;
    core::Solve(*this, rhs, output);
    return output;
}
 
Tensor Tensor::LeastSquares(const Tensor& rhs) const {
    AssertTensorDtypes(*this, {Float32, Float64});
    AssertTensorDevice(rhs, GetDevice());
    AssertTensorDtype(rhs, GetDtype());
 
    Tensor output;
    core::LeastSquares(*this, rhs, output);
    return output;
}
 
std::tuple<Tensor, Tensor, Tensor> Tensor::LU(const bool permute_l) const {
    AssertTensorDtypes(*this, {Float32, Float64});
 
    core::Tensor permutation, lower, upper;
    core::LU(*this, permutation, lower, upper, permute_l);
    return std::make_tuple(permutation, lower, upper);
}
 
std::tuple<Tensor, Tensor> Tensor::LUIpiv() const {
    AssertTensorDtypes(*this, {Float32, Float64});
 
    core::Tensor ipiv, output;
    core::LUIpiv(*this, ipiv, output);
    return std::make_tuple(ipiv, output);
}
 
Tensor Tensor::Triu(const int diagonal) const {
    Tensor output;
    core::Triu(*this, output, diagonal);
    return output;
}
 
Tensor Tensor::Tril(const int diagonal) const {
    Tensor output;
    core::Tril(*this, output, diagonal);
    return output;
}
 
std::tuple<Tensor, Tensor> Tensor::Triul(const int diagonal) const {
    Tensor upper, lower;
    core::Triul(*this, upper, lower, diagonal);
    return std::make_tuple(upper, lower);
}
 
Tensor Tensor::Inverse() const {
    AssertTensorDtypes(*this, {Float32, Float64});
 
    Tensor output;
    core::Inverse(*this, output);
    return output;
}
 
std::tuple<Tensor, Tensor, Tensor> Tensor::SVD() const {
    AssertTensorDtypes(*this, {Float32, Float64});
 
    Tensor U, S, VT;
    core::SVD(*this, U, S, VT);
    return std::tie(U, S, VT);
}
 
}  // namespace core
}  // namespace cloudViewer