code: def convert_to_tensor(x, dtype=None, sparse=None):
      if sparse:
          raise ValueError(
              "`sparse=True` is not supported with torch backend"
          )
      if is_tensor(x):
          device = get_device()
          if x.device != device:
              x = x.to(device)
          if dtype is None:
              return x
          return x.to(to_torch_dtype(dtype))
      if isinstance(x, Variable):
          # TorchDynamo has bugs supporting nn.Parameter type check.
          # Return it directly instead of pass it to the rest of the logic in the
          # function.
          return x.value
      if dtype is None:
          if isinstance(x, bool):
              return torch.as_tensor(
                  x, dtype=torch.bool, device=get_device()
              )
          elif isinstance(x, int):
              return torch.as_tensor(
                  x, dtype=torch.int32, device=get_device()
              )
          elif isinstance(x, float):
              return torch.as_tensor(
                  x, dtype=to_torch_dtype(floatx()), device=get_device()
              )
  
      # Convert to np in case of any array-like that is not list or tuple.
      if not isinstance(x, (list, tuple)):
          x = np.array(x)
      elif len(x) > 0 and any(isinstance(x1, torch.Tensor) for x1 in x):
          # Handle list or tuple of torch tensors
          return torch.stack([convert_to_tensor(x1) for x1 in x])
      if isinstance(x, np.ndarray):
          if x.dtype == np.uint32:
              # Torch backend does not support uint32.
              x = x.astype(np.int64)
          if standardize_dtype(x.dtype) == "bfloat16":
              # Torch backend does not support converting bfloat16 ndarray.
              x = x.astype(np.float32)
              dtype = "bfloat16"
          dtype = dtype or x.dtype
      if dtype is None:
          dtype = result_type(
              *[
                  getattr(item, "dtype", type(item))
                  for item in tree.flatten(x)
              ]
          )
      dtype = to_torch_dtype(dtype)
      return torch.as_tensor(x, dtype=dtype, device=get_device())
  
imports: import contextlib
  import os
  import numpy
  import torch
  import tree
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.dtypes import result_type
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.config import floatx
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 5 for calling a global function with example usage: convert_to_tensor(x, dtype, sparse) and returns: torch, x, x, x, torch, torch, torch, torch