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def get_tile_name_path(dst_dir, index, city=city, code=code):
    '''
    generating specific tile name
    '''
    dst_tile_name = "{}_{}_{}.tif".format(city, code, str(index).zfill(5))
    dst_tile_path = os.path.join(dst_dir, dst_tile_name)
    return dst_tile_name, dst_tile_path

def get_tile_transform(parent_transform, pixel_x,pixel_y):
    '''

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@patch_to(ds.FeatureCollection, cls_method=True)
def read_generic(cls, 
                 fp: str,
                 dst_crs: Union[str, rasterio.crs.CRS]=None,
):
    '''
    geojson reader and can also be used to ensure the geojson in mapped to a destined crs
    '''
    with open(fp, 'r', encoding='utf-8') as f:
        collection = json.load(f)

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# Fastscript usage under various instancees 
# https://github.com/fastai/fastscript/blob/master/00_core.ipynb
# https://fastcore.fast.ai/script.html

from fastcore.all import *

@call_parse 
def test_script(p: Param(help="any basic string", type=str)):
	print(len(p))

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import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import time
import os
import argparse

## Load the model 

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# This is the docker image available. I am using cpu version here. If needed there is gpu version available.
FROM bvlc/caffe:cpu

# Copy the file into docker
COPY requirements.txt requirements.txt

# Run the copied file
RUN pip install -r requirements.txt

# create a folder called model1 and copy all the files in the folder into that folder

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\begin{equation*}
\left[ \begin{array}{cccc}
i_{1} & i_{2} & i_{3} \\\end{array} \right] \times \left[ \begin{array}{cccc}
W_{i1j1} & W_{i1j2} & W_{i1j3} \\
W_{i2j1} & W_{i2j2} & W_{i2j3} \\
W_{i3j1} & W_{i3j2} & W_{i3j3} \\ \end{array} \right] = \left[ \begin{array}{cccc}
h_{1in} & h_{2in} & h_{3in} \\\end{array} \right]
\end{equation*}

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from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256

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from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256
train_data_dir = "data/train"