diff --git a/examples/vision/video_classify.py b/examples/vision/video_classify.py
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+"""
+Title: Video Classification With TimeDistributed Layer
+Author: [Sujoy K Goswami](https://www.linkedin.com/in/sujoy-kumar-goswami/)
+Date created: 2022/01/09
+Last modified: 2022/01/10
+Description: Guide to examine any video-classification-model quickly without any GPU.
+"""
+
+"""
+## Introduction
+
+Video Classification DL Models are heavy and need huge size of data.
+So it is time-consuming and expensive. Here it is shown, how to examine your model
+quickly, before feeding the actual data, and that also without need of any GPU.
+
+Here video dataset will be created; a white rectangle moving in different directions,
+on a black canvas. The sample code for creating left-moving-rectangle videos is below.
+"""
+
+import numpy as np
+import skvideo.io as sk
+from IPython.display import Video
+
+# creating sample video data
+num_vids = 5
+num_imgs = 50
+img_size = 50
+min_object_size = 1
+max_object_size = 5
+
+for i_vid in range(num_vids):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    vid_name = "vid" + str(i_vid) + ".mp4"
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while x > 0:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        x = x - 1
+        i_img = i_img + 1
+    sk.vwrite(vid_name, imgs.astype(np.uint8))
+Video(
+    "vid3.mp4"
+)  # play a video; the script and video generated should be in same folder
+
+"""
+## Data Generation and Preparation
+
+Now dataset with 4 classes will be created where, a rectangle is moving in 4
+different directions in those classes respectively.
+"""
+
+
+# preparing dataset
+X_train = []
+Y_train = []
+labels = {0: "left", 1: "right", 2: "up", 3: "down"}  # 4 classes
+num_vids = 40
+num_imgs = 40
+img_size = 40
+min_object_size = 1
+max_object_size = 5
+# video frames with left moving object
+for i_vid in range(num_vids):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    # vid_name = 'vid' + str(i_vid) + '.mp4'
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while x > 0:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        x = x - 1
+        i_img = i_img + 1
+    X_train.append(imgs)
+for i in range(0, num_imgs):
+    Y_train.append(0)
+# video frames with right moving object
+for i_vid in range(num_vids):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    # vid_name = 'vid' + str(i_vid) + '.mp4'
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while x < img_size:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        x = x + 1
+        i_img = i_img + 1
+    X_train.append(imgs)
+for i in range(0, num_imgs):
+    Y_train.append(1)
+# video frames with up moving object
+for i_vid in range(num_vids):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    # vid_name = 'vid' + str(i_vid) + '.mp4'
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while y > 0:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        y = y - 1
+        i_img = i_img + 1
+    X_train.append(imgs)
+for i in range(0, num_imgs):
+    Y_train.append(2)
+# video frames with down moving object
+for i_vid in range(num_vids):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    # vid_name = 'vid' + str(i_vid) + '.mp4'
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while y < img_size:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        y = y + 1
+        i_img = i_img + 1
+    X_train.append(imgs)
+for i in range(0, num_imgs):
+    Y_train.append(3)
+
+# data pre-processing
+from tensorflow.keras.utils import to_categorical
+
+X_train = np.array(X_train, dtype=np.float32) / 255
+X_train = X_train.reshape(X_train.shape[0], num_imgs, img_size, img_size, 1)
+print(X_train.shape)
+Y_train = np.array(Y_train, dtype=np.uint8)
+Y_train = Y_train.reshape(X_train.shape[0], 1)
+print(Y_train.shape)
+Y_train = to_categorical(Y_train, 4)
+
+"""
+## Model Building and Training
+
+TimeDistributed layer is used to pass temporal information of videos to the network.
+**No GPU is needed.** Training gets completed within few minutes.
+"""
+
+# building model
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import LSTM
+from tensorflow.keras.layers import TimeDistributed
+
+model = Sequential()
+model.add(
+    TimeDistributed(
+        Conv2D(8, (3, 3), strides=(1, 1), activation="relu", padding="same"),
+        input_shape=(num_imgs, img_size, img_size, 1),
+    )
+)
+model.add(
+    TimeDistributed(
+        Conv2D(8, (3, 3), kernel_initializer="he_normal", activation="relu")
+    )
+)
+model.add(TimeDistributed(MaxPooling2D((1, 1), strides=(1, 1))))
+model.add(TimeDistributed(Flatten()))
+model.add(Dropout(0.3))
+model.add(LSTM(64, return_sequences=False, dropout=0.3))
+model.add(Dense(4, activation="softmax"))
+model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+model.summary()
+# model training
+model.fit(X_train, Y_train, epochs=40, verbose=1)
+
+"""
+## Model Inferencing
+
+Model is tested on new generated video data.
+"""
+
+# model testing with new data (4 videos)
+X_test = []
+Y_test = []
+for i_vid in range(2):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while x < img_size:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        x = x + 1
+        i_img = i_img + 1
+    X_test.append(imgs)  # 2nd class - 'right'
+for i_vid in range(2):
+    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
+    w, h = np.random.randint(min_object_size, max_object_size, size=2)
+    x = np.random.randint(0, img_size - w)
+    y = np.random.randint(0, img_size - h)
+    i_img = 0
+    while y < img_size:
+        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
+        y = y + 1
+        i_img = i_img + 1
+    X_test.append(imgs)  # 4th class - 'down'
+X_test = np.array(X_test, dtype=np.float32) / 255
+X_test = X_test.reshape(X_test.shape[0], num_imgs, img_size, img_size, 1)
+pred = np.argmax(model.predict(X_test), axis=-1)
+for i in range(len(X_test)):
+    print(labels[pred[i]])
+
+"""
+Clearly, the model is examined well on this synthetic dataset.
+"""