class_docstr: Concat is a simple PyTorch module for fusing multimodal data by concatenating tensors along dimension 1.
  This fusion method is often used in multimodal learning where data from different modalities (e.g., image, audio)
  are processed separately and then fused together for further processing or decision-making. Each modality data is
  first flattened from its second dimension onward and then these flattened tensors are concatenated together.
  This approach to fusion maintains the independence of the modalities before the fusion point, allowing the network
  to learn separate representations for each modality before combining them.
code: def forward(self, modalities):
      flattened = []
      for modality in modalities:
          flattened.append(torch.flatten(modality, start_dim=1))
      return torch.cat(flattened, dim=1)
  
file_path: pykale/kale/embed/feature_fusion.py
imports: import torch
  from torch import nn
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the Concat obj's underlying member method code with example usage: obj.forward(modalities) and returns: torch