{
					Type:   cType,
					Status: v1.ConditionTrue,
				},
			},
		},
		{
			description: "replace",
			conditions: []v1.PodCondition{
				{
					Type:   cType,
					Status: v1.ConditionTrue,
				},
			},
			condition: v1.PodCondition{
				Type:   cType,
				Status: v1.ConditionFalse,
			},
			wantConditions: []v1.PodCondition{
				{
					Type:   cType,

// and returns an expression that yields the *runtime._type value
// representing that map type.
func MakeMapRType(pos src.XPos, n *ir.MakeExpr) ir.Node {
	assertOp(n, ir.OMAKEMAP)
	if hasRType(n, n.RType, "RType") {
		return n.RType
	}
	return mapRType(pos, n.Type())
}

// MakeSliceElemRType asserts that n is a "make" operation for a slice
// type, and returns an expression that yields the *runtime._type

using tsl::StatusOr;

// Converts the TensorFlow DataType 'dtype' into an MLIR (scalar) type.
Status ConvertDataType(DataType dtype, mlir::Builder builder, mlir::Type* type);

// Converts a scalar MLIR type to a TensorFlow Datatype.
Status ConvertScalarTypeToDataType(mlir::Type type, DataType* dtype);

// Converts an MLIR type to TensorFlow DataType. If 'type' is a scalar type, it

        }
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 1
          }

        }
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 1
          }

  mutex_lock ml(*variable->mu());
  OP_REQUIRES(
      context,
      !variable->is_initialized || variable->tensor()->dtype() == dtype_,
      errors::InvalidArgument(
          "Trying to assign variable with wrong dtype. Expected ",
          DataTypeString(variable->tensor()->dtype()), " got ",
          DataTypeString(dtype_)));
  variable->is_initialized = true;
  *variable->tensor() = value;
}

        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }

                                           DataType dtype, TensorShape shape,
                                           const char* raw_device_name,
                                           ImmediateTensorHandlePtr* handle);

// Executes an AssignVariableOp using `ctx`, assigning the variable associated
// with `variable_handle` with `value`. `dtype` must be the datatype of the

                                     const char* attr_name,
                                     const TF_DataType type,
                                     TF_Status* status) {
  tensorflow::DataType dtype = static_cast<tensorflow::DataType>(type);
  tensorflow::AddTypeConstraint(kernel_builder, attr_name, dtype, status);
}

void TF_KernelBuilder_HostMemory(TF_KernelBuilder* kernel_builder,

// Restores a single non-partioned tensorhandle of dtype `dtype`, using
// checkpoint at `prefix`, with a value stored in `checkpoint_key`.
Status SingleRestore(ImmediateExecutionContext* ctx, const std::string& prefix,
                     const std::string& checkpoint_key, DataType dtype,
                     ImmediateTensorHandlePtr* out);

}  // namespace internal
}  // namespace tensorflow