{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensors\n",
    "```\n",
    "Copyright 2022 National Technology & Engineering Solutions of Sandia,\n",
    "LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the\n",
    "U.S. Government retains certain rights in this software.\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensors are extensions of multidimensial arrays with additional operations defined on them.  Here we explain the basics for creating and working with tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyttb as ttb\n",
    "import numpy as np\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a `tensor` from an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = np.ones((2, 4, 3))  # A 2x4x3 array.\n",
    "X = ttb.tensor(M)  # Convert to a tensor object\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, you can specify a different shape for the `tensor`, so long as the input array has the right number of elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.reshape((4, 2, 3))\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a one-dimensional `tensor`\n",
    "`np.random.rand(m,n)` creates a two-dimensional tensor with `m` rows and `n` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(np.random.rand(5, 1))  # Creates a 2-way tensor.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To specify a 1-way `tensor`, use `(m,)` syntax, signifying a vector with `m` elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(np.random.rand(5), shape=(5,))  # Creates a 1-way tensor.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying trailing singleton dimensions in a `tensor`\n",
    "Likewise, trailing singleton dimensions must be explicitly specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "Y = ttb.tensor(np.random.rand(4, 3))  # Creates a 2-way tensor.\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "Y = ttb.tensor(np.random.rand(3, 4, 1), (3, 4, 1))  # Creates a 3-way tensor.\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The constituent parts of a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 4, 3))  # Create data.\n",
    "X.data  # The array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape  # The shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a `tensor` from its constituent parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 4, 3))  # Create data.\n",
    "Y = X.copy()  # Copies X.\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an empty `tensor`\n",
    "An empty constructor exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ttb.tensor()  # Creates an empty tensor\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `tenones` to create a `tensor` of all ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ttb.tenones((2, 3, 4))  # Creates a 2x3x4 tensor of ones.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `tenzeros` to create a `tensor` of all zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ttb.tenzeros((2, 1, 4))  # Creates a 2x1x4 tensor of zeroes.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `tenrand` to create a random `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 5, 4))\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `squeeze` to remove singleton dimensions from a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 5, 4))  # Create the data.\n",
    "Y = X.copy()\n",
    "# Add singleton dimension.\n",
    "Y[0, 0, 0, 0] = Y[0, 0, 0]\n",
    "# Remove singleton dimension.\n",
    "Y.squeeze().isequal(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `double` to convert a `tensor` to a (multidimensional) array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 5, 4))  # Create the data.\n",
    "X.double()  # Converts X to an array of doubles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.data  # Same thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `ndims` and `shape` to get the shape of a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.ndims  # Number of dimensions (or ways)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape  # Row vector with the shapes of all dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape[2]  # shape of a single dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subscripted reference for a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 3, 4, 1))  # Create a 3x4x2x1 random tensor.\n",
    "X[0, 0, 0, 0]  # Extract a single element."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to extract a subtensor that contains a single element. Observe that singleton dimensions are **not** dropped unless they are specifically specified, e.g., as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0, 0, 0, :]  # Produces a tensor of order 1 and shape 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0, :, 0, :]  # Produces a tensor of shape 3x1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, the subtensor is automatically renumbered/resized in the same way that numpy works for arrays except that singleton dimensions are handled explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0:2, 0, [1, 3], :]  # Produces a tensor of shape 2x2x1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to extract a list of elements by passing in an array of subscripts or a column array of linear indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subs = np.array([[0, 0, 0, 0], [1, 2, 3, 0]])\n",
    "X[subs]  # Extract 2 values by subscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inds = np.array([0, 23])\n",
    "X[inds]  # Same thing with linear indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((10,))  # Create a random tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0:5]  # Extract a subtensor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subscripted assignment for a `tensor\n",
    "We can assign a single element, an entire subtensor, or a list of values for a `tensor`.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 3, 4))  # Create some data.\n",
    "X[0, 0, 0] = 0  # Replaces the [0,0,0] element.\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0, 0:2, 0:2] = np.ones((2, 2))  # Replaces a subtensor.\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[(0, 0, 0)], X[1, 0, 0] = [5, 7]  # Replaces the (0,0,0) and (1,0,0) elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[[0, 1]] = [5, 7]  # Same as above using linear indices.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to **grow** the `tensor` automatically by assigning elements outside the original range of the `tensor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[2, 1, 1] = 1  # Grows the shape of the tensor.\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using negative indexing for the last array index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tenrand((2, 3, 4))  # Create some data.\n",
    "np.prod(X.shape) - 1  # The index of the last element of the flattened tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[2, 2, 3] = 99  # Inserting 99 into last element\n",
    "X[-1]  # Same as X[2,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `find` for subscripts of nonzero elements of a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(3 * np.random.rand(2, 2, 2))  # Generate some data.\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "S, V = X.find()  # Find all the nonzero subscripts and values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "S  # Nonzero subscripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "V  # Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "larger_entries = X >= 2\n",
    "larger_subs, larger_vals = larger_entries.find()  # Find subscripts of values >= 2.\n",
    "larger_subs, larger_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = X[larger_subs]\n",
    "V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the Frobenius norm of a `tensor`\n",
    "`norm` computes the Frobenius norm of a tensor. This corresponds to the Euclidean norm of the vectorized tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(np.ones((3, 2, 3)))\n",
    "X.norm()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `reshape` to rearrange elements in a `tensor`\n",
    "`reshape` reshapes a tensor into a given shape array. The total number of elements in the tensor cannot change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(np.random.rand(3, 2, 3, 10))\n",
    "X.reshape((6, 30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations (plus, minus, and, or, etc.) on a `tensor`\n",
    "`tensor`s support plus, minus, times, divide, power, equals, and not-equals operators.  `tensor`s can use their operators with another `tensor` or a scalar (with the exception of equalities which only takes `tensor`s). All mathematical operators are elementwise operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "A = ttb.tensor(np.floor(3 * np.random.rand(2, 2, 3)))  # Generate some data.\n",
    "B = ttb.tensor(np.floor(3 * np.random.rand(2, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.logical_and(B)  # Calls and."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.logical_or(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.logical_xor(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A == B  # Calls eq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A != B  # Calls neq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A > B  # Calls gt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A >= B  # Calls ge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A < B  # Calls lt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A <= B  # Calls le."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.logical_not()  # Calls not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "+A  # Calls uplus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "-A  # Calls uminus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A + B  # Calls plus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A - B  # Calls minus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A * B  # Calls times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "5 * A  # Calls mtimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A**B  # Calls power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A**2  # Calls power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A / B  # Calls ldivide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2 / A  # Calls rdivide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `tenfun` for elementwise operations on one or more `tensor`s\n",
    "The method `tenfun` applies a specified function to a number of `tensor`s. This can be used for any function that is not predefined for `tensor`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "A = ttb.tensor(np.floor(3 * np.random.rand(2, 2, 3), order=\"F\"))  # Generate some data.\n",
    "A.tenfun(lambda x: x + 1)  # Increment every element of A by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrap np.maximum in a function with a function signature that Python's inspect.signature can handle.\n",
    "def max_elements(a, b):\n",
    "    return np.maximum(a, b)\n",
    "\n",
    "\n",
    "A.tenfun(max_elements, B)  # Max of A and B, elementwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "C = ttb.tensor(\n",
    "    np.floor(5 * np.random.rand(2, 2, 3), order=\"F\")\n",
    ")  # Create another tensor.\n",
    "\n",
    "\n",
    "def elementwise_mean(X):\n",
    "    # finding mean for the columns\n",
    "    return np.floor(np.mean(X, axis=0), order=\"F\")\n",
    "\n",
    "\n",
    "A.tenfun(elementwise_mean, B, C)  # Elementwise means for A, B, and C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `permute` to reorder the modes of a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ttb.tensor(np.arange(1, 25), shape=(2, 3, 4))\n",
    "print(f\"X is a {X}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.permute(np.array((2, 1, 0)))  # Reverse the modes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Permuting a 1-dimensional tensor works correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ttb.tensor(np.arange(1, 5), (4,))\n",
    "X.permute(\n",
    "    np.array(\n",
    "        1,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symmetrizing and checking for symmetry in a `tensor`\n",
    "A `tensor` can be symmetrized in a collection of modes with the command `symmetrize`. The new, symmetric `tensor` is formed by averaging over all elements in the `tensor` which are required to be equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.rand(0)\n",
    "X = ttb.tensor(np.arange(1, 5), (4,))  # Create some data\n",
    "W = ttb.tensor(np.random.rand(4, 4, 4))\n",
    "Y = X.symmetrize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An optional argument `grps` can also be passed to `symmetrize` which specifies an array of modes with respect to which the `tensor` should be symmetrized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X = ttb.tensor(np.random.rand(3, 3, 2))\n",
    "Z = X.symmetrize(np.array((0, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, one can check for symmetry in tensors with the `issymmetric` function. Similar to `symmetrize`, a collection of modes can be passed as a second argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y.issymmetric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z.issymmetric(np.array((1, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying a `tensor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X  # In the python interface"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 1
}