ClientError: An error occurred (ValidationException) when calling the CreateHyperParameterTuningJob operation:The objective metric :[mse]

Getting error: ClientError: An error occurred (ValidationException) when calling the CreateHyperParameterTuningJob operation: The objective metric for the hyperparameter tuning job, [mse], isn’t valid for the [720646828776.dkr.ecr.ap-south-1.amazonaws.com/sagemaker-xgboost:0.90-2-cpu-py3] algorithm. Choose a valid objective metric.

import datetime
import time
import tarfile    
import boto3
import pandas as pd
import numpy as np
from sagemaker import get_execution_role
import sagemaker
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_california_housing
from sagemaker.tuner import (
    IntegerParameter,
    CategoricalParameter,
    ContinuousParameter,
    HyperparameterTuner,
)

s3 = boto3.client("s3")
sm_boto3 = boto3.client("sagemaker")

sagemaker_session = sagemaker.Session()

region = sess.boto_session.region_name

role = get_execution_role()
#Set the required configurations
model_name = "abc_model"
env = "dev"
#S3 Bucket
bucket = "abcpoc"
print("Using bucket " + bucket)


from sagemaker.debugger import Rule, rule_configs
from sagemaker.session import TrainingInput

s3_input_train = TrainingInput(
    s3_data=f"s3://{default_bucket}/train/",content_type="csv")
s3_input_validation = TrainingInput(
    s3_data=f"s3://{default_bucket}/validation/",content_type="csv")
prefix = 'output'

container=sagemaker.image_uris.retrieve("xgboost", region, "1.2-1")
print(container)
xgb = sagemaker.estimator.Estimator(
    image_uri=container,
    role=role,
    base_job_name="xgboost-random-search",
    instance_count=1,
    instance_type="ml.m4.xlarge",
    output_path="s3://{}/{}/output".format(bucket, prefix),
    sagemaker_session= sagemaker.Session(),
    rules=[Rule.sagemaker(rule_configs.create_xgboost_report())]
)


xgb.set_hyperparameters(
    max_depth = 5,
    eta = 0.2,
    gamma = 4,
    min_child_weight = 6,
    subsample = 0.7,
    objective = "reg:squarederror",
    num_round = 1000
)
hyperparameter_ranges = {
    "eta": ContinuousParameter(0, 1),
    "min_child_weight": ContinuousParameter(1, 10),
    "alpha": ContinuousParameter(0, 2),
    "max_depth": IntegerParameter(1, 10),
}

objective_metric_name = "mse"
metric_definitions = [{"Name": "mse", "Regex": "mse: ([0-9\\.]+)"}]

tuner = HyperparameterTuner(estimator, 
                    objective_metric_name, 
                    hyperparameter_ranges, 
                    metric_definitions=None, 
                    strategy='Bayesian', 
                    objective_type='Maximize', 
                    max_jobs=1, 
                    max_parallel_jobs=1, 
                    tags=None, 
                    base_tuning_job_name=None)

#Tune
tuner.fit({
    "train":s3_input_train,
    "validation":s3_input_validation
    },include_cls_metadata=False)

#Explore the best model generated
tuning_job_result = boto3.client("sagemaker").describe_hyper_parameter_tuning_job(
    HyperParameterTuningJobName=tuner.latest_tuning_job.job_name
)

job_count = tuning_job_result["TrainingJobStatusCounters"]["Completed"]
print("%d training jobs have completed" %job_count)
#10 training jobs have completed

#Get the best training job

from pprint import pprint
if tuning_job_result.get("BestTrainingJob",None):
    print("Best Model found so far:")
    pprint(tuning_job_result["BestTrainingJob"])
else:
    print("No training jobs have reported results yet.")