Model doesn't accept int32
I noticed that the pytorch tokenizers output tensors with int64 precision, and the model (I’m experimenting with different transformer models) only accepts int64 tensors, otherwise it throws an error:RuntimeError                              Traceback (most recent call last)in <cell line: 1>()----> 1 model(input_ids = input_ids,  attention_mask=attention_mask, labels=labels)6 frames/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)1516             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]1517         else:→ 1518             return self._call_impl(*args, **kwargs)15191520     def _call_impl(self, *args, **kwargs):/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)1525                 or _global_backward_pre_hooks or _global_backward_hooks1526                 or _global_forward_hooks or _global_forward_pre_hooks):→ 1527             return forward_call(*args, **kwargs)15281529         try:/usr/local/lib/python3.10/dist-packages/transformers/models/t5/modeling_t5.py in forward(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)1776             # move labels to correct device to enable PP1777             labels = labels.to(lm_logits.device)→ 1778             loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))1779             # TODO(thom): Add z_lossmesh/mesh_tensorflow/layers.py at fa19d69eafc9a482aff0b59ddd96b025c0cb207d · tensorflow/mesh · GitHub1780/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)1516             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]1517         else:→ 1518             return self._call_impl(*args, **kwargs)15191520     def _call_impl(self, *args, **kwargs):/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)1525                 or _global_backward_pre_hooks or _global_backward_hooks1526                 or _global_forward_hooks or _global_forward_pre_hooks):→ 1527             return forward_call(*args, **kwargs)15281529         try:/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)11771178     def forward(self, input: Tensor, target: Tensor) → Tensor:→ 1179         return F.cross_entropy(input, target, weight=self.weight,1180                                ignore_index=self.ignore_index, reduction=self.reduction,1181                                label_smoothing=self.label_smoothing)/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)3051     if size_average is not None or reduce is not None:3052         reduction = _Reduction.legacy_get_string(size_average, reduce)→ 3053     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)30543055RuntimeError: expected scalar type Long but found IntIs there a way that allows the model to accept tesnors with preciosn int 32 ? (I’m aware that I can change the input type to int64, but due to memory constraints I would like my input to be int32)