temporal_context=temporal_context.permute(0, 2, 3, 1).contiguous().view(C * V * M,temporal_crop_length)
RuntimeError: shape '[150, 253]' is invalid for input of size 36600

My code is:

def temporal_cropresize(input_data,num_of_frames,l_ratio,output_size):
    C, T, V, M =input_data.shape

    # Temporal crop
    min_crop_length = 64

    scale = np.random.rand(1)*(l_ratio[1]-l_ratio[0])+l_ratio[0]
    temporal_crop_length = np.minimum(np.maximum(int(np.floor(num_of_frames*scale)),min_crop_length),num_of_frames)

    start = np.random.randint(0,num_of_frames-temporal_crop_length+1)
    temporal_context = input_data[:,start:start+temporal_crop_length, :, :]

    # interpolate
    temporal_context = torch.tensor(temporal_context,dtype=torch.float)
    temporal_context=temporal_context.permute(0, 2, 3, 1).contiguous().view(C * V * M,temporal_crop_length)
    temporal_context=temporal_context[None, :, :, None]
    temporal_context= F.interpolate(temporal_context, size=(output_size, 1), mode='bilinear',align_corners=False)
    temporal_context = temporal_context.squeeze(dim=3).squeeze(dim=0)
    temporal_context=temporal_context.contiguous().view(C, V, M, output_size).permute(0, 3, 1, 2).contiguous().numpy()
    return temporal_context