Here is a colab with an example : https://colab.research.google.com/drive/1-EL_YGLPzEPIw_6jU-tWRRLXnx0lN106?usp=sharing 5
import torch
import torch.nn as nn

from ignite.metrics import Metric, Accuracy
from ignite.engine import create_supervised_evaluator


class CustomMetric(Metric):

    _required_output_keys = ("y_pred", "y", "x")

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
    
    def update(self, output):
        print("CustomMetric: output=")
        for i, o in enumerate(output):
            print(i, o.shape)

    def reset(self):
        pass

    def compute(self):
        return 0.0



model = nn.Linear(10, 3)

metrics = {
    "Accuracy": Accuracy(),
    "CustomMetric": CustomMetric()
}

evaluator = create_supervised_evaluator(
    model, 
    metrics=metrics, 
    output_transform=lambda x, y, y_pred: {"x": x, "y": y, "y_pred": y_pred}
)

data = [
    (torch.rand(4, 10), torch.randint(0, 3, size=(4, ))),
    (torch.rand(4, 10), torch.randint(0, 3, size=(4, ))),
    (torch.rand(4, 10), torch.randint(0, 3, size=(4, )))
]
res = evaluator.run(data)

Output:
CustomMetric: output=
0 torch.Size([4, 3])
1 torch.Size([4])
2 torch.Size([4, 10])
CustomMetric: output=
0 torch.Size([4, 3])
1 torch.Size([4])
2 torch.Size([4, 10])
CustomMetric: output=
0 torch.Size([4, 3])
1 torch.Size([4])
2 torch.Size([4, 10])