prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  ThresholdedReLU crashes when the input is a list
  **System information**.
  - Have I written custom code (as opposed to using a stock example script provided in Keras): Yes
  - OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu
  - TensorFlow installed from (source or binary): binary
  - TensorFlow version (use command below): 2.8.0
  - Python version: 3.7.12
  - Bazel version (if compiling from source): N/A
  - GPU model and memory: N/A
  - Exact command to reproduce: https://colab.research.google.com/drive/144FOk8RO-Ew_eBtGZlCmsUL6k0cEAlUO?usp=sharing
  
  **Describe the problem**.
  `keras.layers.ThresholdedReLU` fails to accept a list input by reporting the following error:
  
  ```
  [/usr/local/lib/python3.7/dist-packages/keras/layers/advanced_activations.py](https://localhost:8080/#) in call(self, inputs)
      262 
      263   def call(self, inputs):
  --> 264     theta = tf.cast(self.theta, inputs.dtype)
      265     return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)
      266 
  
  AttributeError: Exception encountered when calling layer "thresholded_re_lu_1" (type ThresholdedReLU).
  
  'list' object has no attribute 'dtype'
  
  Call arguments received:
    • inputs=['tf.Tensor(shape=(None, 1, 10), dtype=float32)', 'tf.Tensor(shape=(None, 1, 10), dtype=float32)', 'tf.Tensor(shape=(None, 1, 10), dtype=float32)']
  ```
  In contrast, `keras.layers.ReLU` and `keras.layers.LeakyReLU` can accept the list input.
  
  **Describe the current behavior**.
  `keras.layers.ThresholdedReLU` crashes when the input is a list
  
  **Describe the expected behavior**.
  ThresholdedReLU can accept the list input.
  
  **[Contributing](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md)**.
  
  - Do you want to contribute a PR? (yes/no):
  - If yes, please read [this page](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md) for instructions
  - Briefly describe your candidate solution(if contributing):
  
  After comparing the code between `ThresholedReLU` and `ReLU`, I think the reason is that `ReLU` directly use the backend implementation: [keras/layers/activation/relu.py#L96](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/layers/activation/relu.py#L96) while ThresholdedReLU implements by itself: [keras/layers/activation/thresholded_relu.py#L63](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/layers/activation/thresholded_relu.py#L63). Not sure why does such an implementation inconsistency exist, but I think we can do something similar in the thresholded_relu.py#L61-63 like [backend.relu](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/backend.py#L4963) does:
  
  ```
  def call(self, inputs):
      dtype = getattr(inputs, 'dtype', floatx())
      theta = tf.cast(self.theta, dtype)
      return inputs * tf.cast(tf.greater(inputs, theta), dtype)
  ```
  
  Of course, we can also directly use the `backend.relu` for the implementation of `ThresholdedReLU` like `ReLU` and `LeakyReLU` do.
  
  **Standalone code to reproduce the issue**.
  You can access this [link](https://colab.research.google.com/drive/144FOk8RO-Ew_eBtGZlCmsUL6k0cEAlUO?usp=sharing) or run the following code:
  
  ```
  import keras
  x = keras.layers.Input(shape=(1,10))
  y = keras.layers.ThresholdedReLU()([x,x,x])
  model = keras.models.Model(x,y)
  model.summary()
  ```
  
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `keras/layers/activation/thresholded_relu.py`
  Content:
  ```
  1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #     http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 # ==============================================================================
  15 """Thresholded Rectified Linear Unit activation layer."""
  16 # pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import
  17 
  18 from keras import backend
  19 from keras.engine.base_layer import Layer
  20 from keras.utils import tf_utils
  21 import tensorflow.compat.v2 as tf
  22 
  23 from tensorflow.python.util.tf_export import keras_export
  24 
  25 
  26 @keras_export('keras.layers.ThresholdedReLU')
  27 class ThresholdedReLU(Layer):
  28   """Thresholded Rectified Linear Unit.
  29 
  30   It follows:
  31 
  32   ```
  33     f(x) = x for x > theta
  34     f(x) = 0 otherwise`
  35   ```
  36 
  37   Input shape:
  38     Arbitrary. Use the keyword argument `input_shape`
  39     (tuple of integers, does not include the samples axis)
  40     when using this layer as the first layer in a model.
  41 
  42   Output shape:
  43     Same shape as the input.
  44 
  45   Args:
  46     theta: Float >= 0. Threshold location of activation.
  47   """
  48 
  49   def __init__(self, theta=1.0, **kwargs):
  50     super(ThresholdedReLU, self).__init__(**kwargs)
  51     if theta is None:
  52       raise ValueError(
  53           'Theta of a Thresholded ReLU layer cannot be None, expecting a float.'
  54           f' Received: {theta}')
  55     if theta < 0:
  56       raise ValueError('The theta value of a Thresholded ReLU layer '
  57                        f'should be >=0. Received: {theta}')
  58     self.supports_masking = True
  59     self.theta = backend.cast_to_floatx(theta)
  60 
  61   def call(self, inputs):
  62     theta = tf.cast(self.theta, inputs.dtype)
  63     return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)
  64 
  65   def get_config(self):
  66     config = {'theta': float(self.theta)}
  67     base_config = super(ThresholdedReLU, self).get_config()
  68     return dict(list(base_config.items()) + list(config.items()))
  69 
  70   @tf_utils.shape_type_conversion
  71   def compute_output_shape(self, input_shape):
  72     return input_shape
  73
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/keras/layers/activation/thresholded_relu.py b/keras/layers/activation/thresholded_relu.py
  --- a/keras/layers/activation/thresholded_relu.py
  +++ b/keras/layers/activation/thresholded_relu.py
  @@ -59,8 +59,8 @@
       self.theta = backend.cast_to_floatx(theta)
   
     def call(self, inputs):
  -    theta = tf.cast(self.theta, inputs.dtype)
  -    return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)
  +    dtype = self.compute_dtype
  +    return inputs * tf.cast(tf.greater(inputs, self.theta), dtype)
   
     def get_config(self):
       config = {'theta': float(self.theta)}
  
verification_info: {"golden_diff": "diff --git a/keras/layers/activation/thresholded_relu.py b/keras/layers/activation/thresholded_relu.py\n--- a/keras/layers/activation/thresholded_relu.py\n+++ b/keras/layers/activation/thresholded_relu.py\n@@ -59,8 +59,8 @@\n     self.theta = backend.cast_to_floatx(theta)\n \n   def call(self, inputs):\n-    theta = tf.cast(self.theta, inputs.dtype)\n-    return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)\n+    dtype = self.compute_dtype\n+    return inputs * tf.cast(tf.greater(inputs, self.theta), dtype)\n \n   def get_config(self):\n     config = {'theta': float(self.theta)}\n", "issue": "ThresholdedReLU crashes when the input is a list\n**System information**.\r\n- Have I written custom code (as opposed to using a stock example script provided in Keras): Yes\r\n- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu\r\n- TensorFlow installed from (source or binary): binary\r\n- TensorFlow version (use command below): 2.8.0\r\n- Python version: 3.7.12\r\n- Bazel version (if compiling from source): N/A\r\n- GPU model and memory: N/A\r\n- Exact command to reproduce: https://colab.research.google.com/drive/144FOk8RO-Ew_eBtGZlCmsUL6k0cEAlUO?usp=sharing\r\n\r\n**Describe the problem**.\r\n`keras.layers.ThresholdedReLU` fails to accept a list input by reporting the following error:\r\n\r\n```\r\n[/usr/local/lib/python3.7/dist-packages/keras/layers/advanced_activations.py](https://localhost:8080/#) in call(self, inputs)\r\n    262 \r\n    263   def call(self, inputs):\r\n--> 264     theta = tf.cast(self.theta, inputs.dtype)\r\n    265     return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)\r\n    266 \r\n\r\nAttributeError: Exception encountered when calling layer \"thresholded_re_lu_1\" (type ThresholdedReLU).\r\n\r\n'list' object has no attribute 'dtype'\r\n\r\nCall arguments received:\r\n  \u2022 inputs=['tf.Tensor(shape=(None, 1, 10), dtype=float32)', 'tf.Tensor(shape=(None, 1, 10), dtype=float32)', 'tf.Tensor(shape=(None, 1, 10), dtype=float32)']\r\n```\r\nIn contrast, `keras.layers.ReLU` and `keras.layers.LeakyReLU` can accept the list input.\r\n\r\n**Describe the current behavior**.\r\n`keras.layers.ThresholdedReLU` crashes when the input is a list\r\n\r\n**Describe the expected behavior**.\r\nThresholdedReLU can accept the list input.\r\n\r\n**[Contributing](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md)**.\r\n\r\n- Do you want to contribute a PR? (yes/no):\r\n- If yes, please read [this page](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md) for instructions\r\n- Briefly describe your candidate solution(if contributing):\r\n\r\nAfter comparing the code between `ThresholedReLU` and `ReLU`, I think the reason is that `ReLU` directly use the backend implementation: [keras/layers/activation/relu.py#L96](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/layers/activation/relu.py#L96) while ThresholdedReLU implements by itself: [keras/layers/activation/thresholded_relu.py#L63](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/layers/activation/thresholded_relu.py#L63). Not sure why does such an implementation inconsistency exist, but I think we can do something similar in the thresholded_relu.py#L61-63 like [backend.relu](https://github.com/keras-team/keras/blob/90aa76f6c48ec5252b2db7926ff060e262dfc1cc/keras/backend.py#L4963) does:\r\n\r\n```\r\ndef call(self, inputs):\r\n    dtype = getattr(inputs, 'dtype', floatx())\r\n    theta = tf.cast(self.theta, dtype)\r\n    return inputs * tf.cast(tf.greater(inputs, theta), dtype)\r\n```\r\n\r\nOf course, we can also directly use the `backend.relu` for the implementation of `ThresholdedReLU` like `ReLU` and `LeakyReLU` do.\r\n\r\n**Standalone code to reproduce the issue**.\r\nYou can access this [link](https://colab.research.google.com/drive/144FOk8RO-Ew_eBtGZlCmsUL6k0cEAlUO?usp=sharing) or run the following code:\r\n\r\n```\r\nimport keras\r\nx = keras.layers.Input(shape=(1,10))\r\ny = keras.layers.ThresholdedReLU()([x,x,x])\r\nmodel = keras.models.Model(x,y)\r\nmodel.summary()\r\n```\r\n\r\n\n", "before_files": [{"content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Thresholded Rectified Linear Unit activation layer.\"\"\"\n# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import\n\nfrom keras import backend\nfrom keras.engine.base_layer import Layer\nfrom keras.utils import tf_utils\nimport tensorflow.compat.v2 as tf\n\nfrom tensorflow.python.util.tf_export import keras_export\n\n\n@keras_export('keras.layers.ThresholdedReLU')\nclass ThresholdedReLU(Layer):\n  \"\"\"Thresholded Rectified Linear Unit.\n\n  It follows:\n\n  ```\n    f(x) = x for x > theta\n    f(x) = 0 otherwise`\n  ```\n\n  Input shape:\n    Arbitrary. Use the keyword argument `input_shape`\n    (tuple of integers, does not include the samples axis)\n    when using this layer as the first layer in a model.\n\n  Output shape:\n    Same shape as the input.\n\n  Args:\n    theta: Float >= 0. Threshold location of activation.\n  \"\"\"\n\n  def __init__(self, theta=1.0, **kwargs):\n    super(ThresholdedReLU, self).__init__(**kwargs)\n    if theta is None:\n      raise ValueError(\n          'Theta of a Thresholded ReLU layer cannot be None, expecting a float.'\n          f' Received: {theta}')\n    if theta < 0:\n      raise ValueError('The theta value of a Thresholded ReLU layer '\n                       f'should be >=0. Received: {theta}')\n    self.supports_masking = True\n    self.theta = backend.cast_to_floatx(theta)\n\n  def call(self, inputs):\n    theta = tf.cast(self.theta, inputs.dtype)\n    return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)\n\n  def get_config(self):\n    config = {'theta': float(self.theta)}\n    base_config = super(ThresholdedReLU, self).get_config()\n    return dict(list(base_config.items()) + list(config.items()))\n\n  @tf_utils.shape_type_conversion\n  def compute_output_shape(self, input_shape):\n    return input_shape\n", "path": "keras/layers/activation/thresholded_relu.py"}], "after_files": [{"content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Thresholded Rectified Linear Unit activation layer.\"\"\"\n# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import\n\nfrom keras import backend\nfrom keras.engine.base_layer import Layer\nfrom keras.utils import tf_utils\nimport tensorflow.compat.v2 as tf\n\nfrom tensorflow.python.util.tf_export import keras_export\n\n\n@keras_export('keras.layers.ThresholdedReLU')\nclass ThresholdedReLU(Layer):\n  \"\"\"Thresholded Rectified Linear Unit.\n\n  It follows:\n\n  ```\n    f(x) = x for x > theta\n    f(x) = 0 otherwise`\n  ```\n\n  Input shape:\n    Arbitrary. Use the keyword argument `input_shape`\n    (tuple of integers, does not include the samples axis)\n    when using this layer as the first layer in a model.\n\n  Output shape:\n    Same shape as the input.\n\n  Args:\n    theta: Float >= 0. Threshold location of activation.\n  \"\"\"\n\n  def __init__(self, theta=1.0, **kwargs):\n    super(ThresholdedReLU, self).__init__(**kwargs)\n    if theta is None:\n      raise ValueError(\n          'Theta of a Thresholded ReLU layer cannot be None, expecting a float.'\n          f' Received: {theta}')\n    if theta < 0:\n      raise ValueError('The theta value of a Thresholded ReLU layer '\n                       f'should be >=0. Received: {theta}')\n    self.supports_masking = True\n    self.theta = backend.cast_to_floatx(theta)\n\n  def call(self, inputs):\n    dtype = self.compute_dtype\n    return inputs * tf.cast(tf.greater(inputs, self.theta), dtype)\n\n  def get_config(self):\n    config = {'theta': float(self.theta)}\n    base_config = super(ThresholdedReLU, self).get_config()\n    return dict(list(base_config.items()) + list(config.items()))\n\n  @tf_utils.shape_type_conversion\n  def compute_output_shape(self, input_shape):\n    return input_shape\n", "path": "keras/layers/activation/thresholded_relu.py"}]}