Chollet Discusses the Types of Tensors Typically Encountered in Deep $\bullet$

Chollet discusses the types of tensors typically encountered in deep learning: $\bullet$ A 0D (0-dimensional) tensor is one value and is known as a xe "scalar value"scalar.
$\bullet$ A 1D tensor is similar to a one-dimensional array and is known as a xe "vector"vector. A 1D tensor might represent a sequence, such as hourly temperature readings from a sensor or the words of one movie review.
$\bullet$ A 2D tensor is similar to a two-dimensional array and is known as a xe "matrix"matrix. A 2D tensor could represent a grayscale image in which the tensor's two dimensions are the image's width and height in pixels, and the value in each element is the intensity of that pixel.
Which of the following statements a) , b) or c) about additional types of tensors is false?

A) A 3D tensor is similar to a three-dimensional array and could be used to represent a color image. The first two dimensions would represent the width and height of the image in pixels and the depth at each location might represent the red, green and blue (RGB) components of a given pixel's color. A 3D tensor also could represent a collection of 2D tensors containing grayscale images.
B) A 4D tensor could be used to represent a collection of color images in 3D tensors. It also could be used to represent one video. Each frame in a video is essentially a color image.
C) A 5D tensor could be used to represent a collection of 4D tensors containing videos.
D) All of the above statements are true.