what this code does? and improve it
using System;
using System.Threading.Tasks;
using System.Numerics; // For INumber<T>
using System.Collections.Generic;
using System.Buffers;

namespace HyperMath.src.Core
{
    /// <summary>
    /// Represents a multi-dimensional tensor with generic type T.
    /// </summary>
    /// <typeparam name="T">The type of elements in the tensor, must implement INumber<T> and IExponentialFunctions<T>.</typeparam>
    public class Tensor<T> where T : struct, INumber<T>, IExponentialFunctions<T>, IComparable, IConvertible, IEquatable<T>, IFormattable
    {

        private T[] data;
        public int[] Shape { get; private set; }
        public int TotalSize { get; private set; }

        public static int ParallelizationThreshold { get; set; } = 1000;

        public Tensor(params int[] dimensions)
        {
            Shape = dimensions;
            TotalSize = Shape.Aggregate(1, (a, b) => a * b);
            data = ArrayPool<T>.Shared.Rent(TotalSize);
        }

        public void Dispose()
        {
            ArrayPool<T>.Shared.Return(data);
            data = null;
        }

        public T this[params int[] indices]
        {
            get
            {
                int flatIndex = CalculateFlatIndex(indices);
                return data[flatIndex];
            }
            set
            {
                int flatIndex = CalculateFlatIndex(indices);
                data[flatIndex] = value;
            }
        }

        private int CalculateFlatIndex(int[] indices)
        {
            if (indices.Length != Shape.Length)
                throw new ArgumentException("Incorrect number of indices.");

            int flatIndex = 0;
            for (int i = 0; i < indices.Length; i++)
            {
                if (indices[i] >= Shape[i])
                    throw new ArgumentOutOfRangeException($"Index {indices[i]} out of bounds for dimension {i}.");
                flatIndex = flatIndex * Shape[i] + indices[i];
            }
            return flatIndex;
        }

        private bool IsSameShape(Tensor<T> other)
        {
            if (Shape.Length != other.Shape.Length) return false;
            for (int i = 0; i < Shape.Length; i++)
            {
                if (Shape[i] != other.Shape[i]) return false;
            }
            return true;
        }

        public Tensor<T> ElementWiseAdd(Tensor<T> other)
        {
            if (!IsSameShape(other)) throw new ArgumentException("Dimension mismatch.");

            Tensor<T> result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] +  other.data[i];
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] +  other.data[i];
                }
            }
            return result;
        }

        public Tensor<T> ElementWiseMultiply(Tensor<T> other)
        {
            if (!IsSameShape(other)) throw new ArgumentException("Dimension mismatch.");

            Tensor<T> result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] *  other.data[i];
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] *  other.data[i];
                }
            }
            return result;
        }

        public Tensor<T> ElementWiseSubtract(Tensor<T> other)
        {
            if (!IsSameShape(other)) throw new ArgumentException("Dimension mismatch.");

            Tensor<T> result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] -  other.data[i];
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] -  other.data[i];
                }
            }
            return result;
        }

        public Tensor<T> ElementWiseDivide(Tensor<T> other)
        {
            if (!IsSameShape(other)) throw new ArgumentException("Dimension mismatch.");

            Tensor<T> result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] /  other.data[i];
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] /  other.data[i];
                }
            }
            return result;
        }

        public Tensor<T> DotProduct(Tensor<T> other, int[]? thisAxes = null, int[]? otherAxes = null)
        {
            // Default axis handling (if not specified, assume the last axis of 'this' and the first axis of 'other')
            if (thisAxes == null) thisAxes = new[] { Shape.Length - 1 };
            if (otherAxes == null) otherAxes = new[] { 0 };

            // Ensure the axis counts match
            if (thisAxes.Length != otherAxes.Length)
                throw new ArgumentException("The number of axes to contract must be the same for both tensors.");

            // Check dimensions for the contracted axes
            for (int i = 0; i < thisAxes.Length; i++)
            {
                if (Shape[thisAxes[i]] != other.Shape[otherAxes[i]])
                    throw new ArgumentException("The dimensions of the tensors do not match for the specified axes.");
            }

            // Calculate the resulting shape after contraction
            var resultShape = Shape
                .Where((_, index) => !thisAxes.Contains(index))
                .Concat(other.Shape.Where((_, index) => !otherAxes.Contains(index)))
                .ToArray();

            var result = new Tensor<T>(resultShape);

            // Iterate over the resulting tensor and perform the contraction
            if (result.TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, result.TotalSize, index =>
                {
                    var resultIndices = UnflattenIndex(index, resultShape);
                    dynamic sum = default(T);

                    var leftIndices = resultIndices.Take(Shape.Length - thisAxes.Length).ToArray();
                    var rightIndices = resultIndices.Skip(Shape.Length - thisAxes.Length).ToArray();

                    for (int k = 0; k < Shape[thisAxes[0]]; k++) // iterate over contraction dimension
                    {
                        var fullLeftIndices = leftIndices.ToList();
                        var fullRightIndices = rightIndices.ToList();

                        foreach (var axis in thisAxes) fullLeftIndices.Insert(axis, k);
                        foreach (var axis in otherAxes) fullRightIndices.Insert(axis, k);

                        sum +=  this[fullLeftIndices.ToArray()] *  other[fullRightIndices.ToArray()];
                    }

                    result[resultIndices] = sum;
                });
            }
            else
            {
                for (int index = 0; index < result.TotalSize; index++)
                {
                    var resultIndices = UnflattenIndex(index, resultShape);
                    dynamic sum = default(T);

                    var leftIndices = resultIndices.Take(Shape.Length - thisAxes.Length).ToArray();
                    var rightIndices = resultIndices.Skip(Shape.Length - thisAxes.Length).ToArray();

                    for (int k = 0; k < Shape[thisAxes[0]]; k++) // iterate over contraction dimension
                    {
                        var fullLeftIndices = leftIndices.ToList();
                        var fullRightIndices = rightIndices.ToList();

                        foreach (var axis in thisAxes) fullLeftIndices.Insert(axis, k);
                        foreach (var axis in otherAxes) fullRightIndices.Insert(axis, k);

                        sum +=  this[fullLeftIndices.ToArray()] *  other[fullRightIndices.ToArray()];
                    }

                    result[resultIndices] = sum;
                }
            }

            return result;
        }

        // Utility method to convert a flat index to multi-dimensional indices
        private static int[] UnflattenIndex(int flatIndex, int[] shape)
        {
            int[] indices = new int[shape.Length];
            for (int i = shape.Length - 1; i >= 0; i--)
            {
                indices[i] = flatIndex % shape[i];
                flatIndex /= shape[i];
            }
            return indices;
        }

        // Element-wise scalar addition
        public Tensor<T> AddScalar(T scalar)
        {
            var result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] +  scalar;
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] +  scalar;
                }
            }
          
            return result;
        }

        // Element-wise scalar subtraction
        public Tensor<T> SubtractScalar(T scalar)
        {
            var result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] -  scalar;
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] -  scalar;
                }
            }
            return result;
        }

        // Element-wise scalar multiplication
        public Tensor<T> MultiplyScalar(T scalar)
        {
            var result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] *  scalar;
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] *  scalar;
                }
            }
            return result;
        }

        // Element-wise scalar division
        public Tensor<T> DivideScalar(T scalar)
        {
            var result = new Tensor<T>(Shape);

            if (TotalSize >= ParallelizationThreshold)
            {
                Parallel.For(0, TotalSize, i =>
                {
                    result.data[i] =  data[i] /  scalar;
                });
            }
            else
            {
                for (int i = 0; i < TotalSize; i++)
                {
                    result.data[i] =  data[i] /  scalar;
                }
            }
            return result;
        }

        public override string ToString()
        {
            return $"Tensor with shape [{string.Join(", ", Shape)}]";
        }
    }
}