Module initialization error: An error occurred (403) when calling the HeadObject operation: Forbidden

Hello, everyone. I am trying to deploy main Text to speech trained model on AWS using lambda function.
There this error while loading my model from s3 bucket. I am also providing the code which of lambda function I have created and this error is occurring while testing the code in test configuration. Please help me resolve this issue.

	import unzip_requirements
except ImportError:
	print('Unable to import');
import json
import urllib.parse
import boto3

print('Loading function')

s3 = boto3.client('s3')
import torch
import numpy as np
#from import write
from os import path

my_bucket = 'modeltts'
orig_file = 'TTS_Nigerian'
dest_file = '/'

s3 = boto3.resource('s3')
s3.Bucket(my_bucket).download_file(orig_file, dest_file)

model_path= dest_file
tacotron2 = torch.hub.load('nvidia/DeepLearningExamples:torchhub', 'nvidia_tacotron2')
tacotron2 ='cuda')

def lambda_handler(event, context):
    #print("Received event: " + json.dumps(event, indent=2))

    # Get the object from the event and show its content type
    #bucket = event['Records'][0]['s3']['bucket']['name']
    #key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8')
        #response = s3.get_object(Bucket=bucket, Key=key)
        #print("CONTENT TYPE: " + response['ContentType'])
        #return response['ContentType']
    #except Exception as e:
        #print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
        #raise e
    #s3 = boto3.resource('s3')
    #with open('TTS_Nigerian', wb) as tacotron2:
        #s3.Bucket(bucket).download_file(key, tacotron2)          
    #tacotron2 = load_model()
    text = "Dear User, Your guider got closed unexpectedly.Would you like to continue from where you left off."

    utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tts_utils')
    sequence, lengths = utils.prepare_input_sequence([text])

    with torch.no_grad():
        mel_output_postnet, _, alignment = tacotron2.infer(sequence, lengths)
        audio = waveglow.infer(mel_output_postnet)
    audio_numpy = audio[0].data.cpu().numpy()
    sampling_rate = 22050
    from IPython.display import Audio
    Audio(audio_numpy, rate=sampling_rate)

@ptrblck please acknowledge