return true;
      }
    }
  }
  return false;
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
void GradientTape<Gradient, BackwardFunction, TapeTensor>::Watch(
    int64_t tensor_id) {
  tensor_tape_.emplace(tensor_id, -1);
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>

      for (int i = 0; i < num_values; ++i) {
        op_->colocation_constraints.emplace(static_cast<const char*>(values[i]),
                                            lengths[i]);
      }
    } else {
      std::vector<tensorflow::StringPiece> v;
      v.reserve(num_values);
      for (int i = 0; i < num_values; ++i) {
        v.emplace_back(static_cast<const char*>(values[i]), lengths[i]);
      }

    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

// Runs `model` maybe wrapped in a function.
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,

    input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }
  desc->node_builder.Input(input_list);
}

void TF_AddControlInput(TF_OperationDescription* desc, TF_Operation* input) {
  desc->node_builder.ControlInput(&input->node);
}

void TF_ColocateWith(TF_OperationDescription* desc, TF_Operation* op) {
  desc->colocation_constraints.emplace(

                                    "Encountered while processing input ", i,
                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;

    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;

interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces

    env:
    {{- range $key, $value := .ProxyConfig.ProxyMetadata }}
    - name: {{ $key }}
      value: "{{ $value }}"
    {{- end }}
  {{- end }}
    resources:
  {{ template "resources" . }}
    securityContext:
      allowPrivilegeEscalation: {{ .Values.global.proxy.privileged }}
      privileged: {{ .Values.global.proxy.privileged }}
      capabilities:

    env:
    {{- range $key, $value := .ProxyConfig.ProxyMetadata }}
    - name: {{ $key }}
      value: "{{ $value }}"
    {{- end }}
  {{- end }}
    resources:
  {{ template "resources" . }}
    securityContext:
      allowPrivilegeEscalation: {{ .Values.global.proxy.privileged }}
      privileged: {{ .Values.global.proxy.privileged }}
      capabilities:

  // It must match the pod template's labels.
  optional k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector selector = 2;

  // Template describes the pods that will be created.
  // The only allowed template.spec.restartPolicy value is "Always".
  optional k8s.io.api.core.v1.PodTemplateSpec template = 3;

  // The deployment strategy to use to replace existing pods with new ones.
  // +optional